{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b33fc476-c6ed-4c8d-9a71-0decdb536f1d",
   "metadata": {},
   "source": [
    "# KNN 实现聚类（监督学习）\n",
    "2D 数据类别划分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2990f033-eb0a-4fc3-a502-6d59c5852e26",
   "metadata": {},
   "source": [
    "## KMeans Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52dced70-334c-4456-a370-998972e7b8fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.072345</td>\n",
       "      <td>-3.241693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.936710</td>\n",
       "      <td>15.784810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.083576</td>\n",
       "      <td>7.319176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.120670</td>\n",
       "      <td>14.406780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.711550</td>\n",
       "      <td>2.557729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          V1         V2  labels\n",
       "0   2.072345  -3.241693       0\n",
       "1  17.936710  15.784810       0\n",
       "2   1.083576   7.319176       0\n",
       "3  11.120670  14.406780       0\n",
       "4  23.711550   2.557729       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('k_mean_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dbac947e-c273-4e00-b542-e8fd314439dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labels\n",
       "2    1156\n",
       "1     954\n",
       "0     890\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义数据\n",
    "\n",
    "X = data.drop(['labels'],axis = 1)\n",
    "y = data.loc[:,'labels']\n",
    "pd.Series(y).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e6cfd49f-a8fe-43ab-8d18-058e621eb589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据可视化\n",
    "#matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure()\n",
    "plt.scatter(X.loc[:,'V1'], X.loc[:,'V2'])\n",
    "plt.title('un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d122ab6-b63f-41ab-8284-25f624d2f43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0], X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1], X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2], X.loc[:,'V2'][y==2])\n",
    "plt.title('un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71d6f7d0-cb56-41d0-a98d-355941ae73db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 2) (3000,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c53dea8f-58c6-4b26-b31c-2d3daa70c69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立KMeans模型\n",
    "from sklearn.cluster import KMeans\n",
    "KM = KMeans(n_clusters=3, random_state=0)\n",
    "KM.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "36fa3f6a-a116-4370-971f-6d6f6435a0bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 40.68362784  59.71589274]\n",
      " [ 69.92418447 -10.11964119]\n",
      " [  9.4780459   10.686052  ]]\n"
     ]
    }
   ],
   "source": [
    "print(KM.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a741672d-a803-4344-9721-ee33b60949c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = KM.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "383bd8b7-1d2f-43fd-95e5-6d767f8aaf96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8vAzvTBwSPgRBeDRda3XFgrQFiFPHmWyPV8djQdoCQ8WSM0q5+fwZa1QNror0GulOm4MtPCEqRthHXHiIrOPs4c8//8T169cxe/ZsPPbYY6hfv75gYjMA7N+/3/D/hYWFOHPmDOrXrw8ASE1NxYkTJ/Dwww9bfAUHB1scq3nz5ggKCkJmZqZhW15eHrKzs9GuXTuZ36UpVM5OEIRHYu6SvOmpTTh2/Ziga7Jcpdzm59RxOqv5MwBwq/IWPjn2CYIUQfj2zLcoKHtw03BFOTkfFRu3axwUUJgkObsqKkbYR6ukaCREhiC/qFwwz0cBQBOpL213FjVr1kRwcDAWL16M4cOHIzs7GzNmzBAc++677yImJgbx8fGYMmUKYmNj8eSTTwIAJk6ciDZt2mDkyJEYNmwYwsLCcOrUKWRmZmLx4sUWx4qMjMTQoUMxfvx4xMTEIDo6Gm+++SYaNWqErl2da79AwocgCI/DmpDpWaenxdhxu8ZZVDXxpdzGUSGp54wMFs/tMWbpH0sFt18tvYo3dr2BDzp9gO61uwuOkQM+KiZ0zSa2mkg+Ph5KgFKBqX2S8dqaI1AAJp9gPn43tU+yU/18qlWrhlWrVuHtt9/GokWLkJqaivnz56Nv374WY2fPno0xY8bg7NmzaNKkCTZs2GCI5jRu3Bi7d+/GlClT8Nhjj4HjONStWxfPP/+86Lk//PBDBAYG4rnnnkNZWRm6dOmCVatWOdXDBwAUHMc5N13cyyguLkZkZCSKiooQERHh7ukQhN8hJmT46IX58lbG2gzRqIwCCsSr47G532arEQ+xc8qFUqHEvI7znCp+AOol5mrKy8uRk5ODpKQkhITYvxy1OTsP0388aZLonBAZgql9ktEjJUGOqfoM1q456/2bIj4EQXgMUl2SpZRyt9S0lHxOudBxOozfPR4fKj50+rKX2PskPJceKQnolqzxO+dmd0HJzQRBeAxSPWnkKOW2dU45ISNBQowApQJt68bgiabV0bZuDIkeJ0LChyAIj0GqkGEt0Y4OiRattmI9Z2hAKNM4a5CRIEG4H1rqIgjCY5DqScOXclvL8YlURWLKnimi1Vas5yzTlgEAwgLDUHJPuP8QC2QkSBDuhSI+BEF4DFI9aQKUAeiZ1FNwLKBfGrtVcctE9ACmzTttndMcR0QPQEaCBOFuSPgQBOExSHFq1uq0+PTYp1h5YqXo8dSBasHtxs07AYie0xZSxpuLNmOzw/1X9uNA3gFR40N3GCMShK9CS10EQXgULJ4023K3YdaBWRaRHHNK75WKvmacKC12TltUVVVFYUWhyfe3Km5ZjDMXbUKeQcYYL8XJZc5IEIQerxE+9+7dw7Rp0/D5558jPz8fCQkJGDx4MP71r39BqdQHrjiOw/Tp07F06VIUFhaidevW+Pjjj9GwYUM3z54gCGNs+c10rdUV6TXSBcfI7bnD59wYnzPzQia+PP2lzX17JvVEl1pdTOa489JOC6GiDlSjXWI7hAeHY+uFrXhz95tW588vxQ1uOBirTqxy2JzRWZBvEOGNeI3wmTNnDj799FOsXr0aDRs2xKFDh/Dyyy8jMjISY8aMAQDMnTsXCxYswKpVq/DII4/gvffeQ7du3XD69GmEh4e7+R0QhP+g1WlxMP8gNvy1AaX3SpEal4r+9fsjODBYMIIRpYpC7zq9kV4z3XDzFPKkcYbnjnHOjfE5WYTPzxd+xoSWEwRF27Ljy7AyeyVK75Wi5F4JMi9mIvNipkVbCSH411efXM3saSSGs8QJRaIIb8VrnJt79+6N+Ph4LF++3LCtX79+UKvV+Oyzz8BxHBITEzF27FhMnDgRAFBRUYH4+HjMmTMHr776KtN5yLmZIBxjW+42TNkzxWKZSQEF0mqkYdelXVZv/NZunln5WRiyZYgs87Tm6qzVaZH+TbrJMpYYKzJWWAi0bbnb8MauN2SZpz3nN56HM8SJFHdtX0Yu52Z3kJaWhqZNm2LhwoU2x+7atQvp6ekoLCxE1apV7T5n7dq1MXbsWIwdO9buY8jh3Ow1yc0dOnTA9u3bcebMGQDAsWPHsGfPHvTsqa/oyMnJQX5+Prp3f2AJr1Kp0KlTJ+zdu1f0uBUVFSguLjb5IgjCPvgbvlBuDQcOOy/ttBntuFp6FeN2jcOnxz61SOaVWgpurToMEG/eGaAMQO86vZnOYT4nlo7uciJ2TXhxYp5HZFzRZg+23LUBMmok2Fi6dCnS0tIQEREBhUKBW7duueS8XrPUNXHiRBQVFaF+/foICAiAVqvF+++/j/79+wMA8vPzAQDx8fEm+8XHxyM3N1f0uLNmzcL06dOdN3GC8BO0Oi1m7p8py7E4cPj46MeG7+PV8Xir5VuShY+YyIpURWJq26lWoxIRKraIr3l5uiudoIXOD0hv/SEFOdqEEALotEDuXuDOVaBKPFCrHeDj+VKlpaXo0aMHevTogcmTJ7vsvF4T8fn666+xZs0afPHFFzhy5AhWr16N+fPnY/Xq1SbjFArTJzyO4yy2GTN58mQUFRUZvi5duuSU+ROEr3Ok4AiulTvHnO9q6VWM3z0e8w/PZxqvtPGnLVgRjCpBVQwRpcp7lSbl4lsvbMUnRz+xeR7j8nQeOQ0KlQols6eRMVJbf0hBjjYhhBknNwALU4DVvYG1Q/X/LkzRb3cBa9asQYsWLRAeHg6NRoMBAwagoMCyYvK3335DkyZNEBISgtatW+P48eMmr+/duxcdO3ZEaGgoatSogdGjR6OkRNz3auzYsZg0aRLatGkj+3uyhtdEfCZMmIBJkybhhRdeAAA0atQIubm5mDVrFgYNGgSNRgMAhoovnoKCAosokDEqlQoqlcq5kycIP8BTbnRP1n0S68+ttzqmoLwAwzKHGb5XKpTQcTqT71kSqB9PetwiYiKXQaECCgxKHoRVJ1ZZJETbWqpzpjiR6q5N2ODkBuCbfwDmn7fiPP325/4HJPd16hQqKysxY8YMPProoygoKMAbb7yBwYMHY9OmTSbjJkyYgH//+9/QaDR4++230bdvX5w5cwZBQUE4fvw4MjIyMGPGDCxfvhzXrl3DqFGjMGrUKKxcKe615Q68JuJTWlpqKFvnCQgIgE6n/2OVlJQEjUaDzMxMw+uVlZXYvXs32rVr59K5EoQ/EhMa49bzx6vj8WHah2ib2FbyvsaiR+h7MVadWIWtF7aaRIuaxDZBvFr8YYuF+NB4LEhbgHEt9CXrceo409fV8VYTiJ0pTqS6axNW0GmBzRNhIXqAB9s2T9KPcyJDhgzB448/jjp16qBNmzZYtGgRfv75Z9y5c8dk3NSpU9GtWzc0atQIq1evxtWrV7Fu3ToAwLx58zBgwACMHTsW9erVQ7t27bBo0SL873//Q3l5uVPnLxWvifj06dMH77//PmrWrImGDRvi999/x4IFCzBkiL7CQ6FQYOzYsZg5cybq1auHevXqYebMmVCr1RgwYICbZ08Qvg1fPeROBiYPRKW2EjfKb7jsnBw4TPhlgolQilfHo2dST6uO0tYY2XQkhjUaZojkWPM0EoMXJwWlBYKRK76izR5xwrtrj9s1TnIkijAjdy9QfMXKAA4ovqwfl/SY06bx+++/Y9q0aTh69Chu3rxpCChcvHgRycnJhnFt2z54qIiOjsajjz6KU6dOAQAOHz6Mv/76C59//vmD2XMcdDodcnJy0KBBA6fNXypeI3wWL16Md955ByNGjEBBQQESExPx6quv4v/+7/8MY9566y2UlZVhxIgRBgPDrVu3kocPQTgRuQ0F7WX+oQf5P+ZLV87E/DwFpQVYdWIVXm74Mr7/63sUVRQxHSc6JBrvtHlHMIpj7C/E+/JcLbmKwopCRKmiEB8WbyKGnC1OWNy1CQbuMCbBs46zg5KSEnTv3h3du3fHmjVrUK1aNVy8eBEZGRmorKy0uT+fQ6vT6fDqq69i9OjRFmNq1qwp+7wdwWuET3h4OBYuXGjVc0ChUGDatGmYNm2ay+ZFEN6Go4Z2xvtHh0Rj1oFZbhc95rhK9AjBX4vv//oe8zrOw4G8A1ievdzGXsCEFhNsCgZrrS7M/XmcLU7siUQRZlRhXBJlHWcHf/75J65fv47Zs2ejRo0aAIBDhw4Jjt2/f79BxBQWFuLMmTOoX78+ACA1NRUnTpzAww8/7LS5yoXXCB+CIKRjLnIKywsxN2uu3YZ2tnpMeRqujPyYU1RRhH9m/hNRqiim8fFh1m9utiJrvP+Rce6Ps8WJkLs2IYFa7YCIRH0is+DPVaF/vZbz8lRr1qyJ4OBgLF68GMOHD0d2djZmzJghOPbdd99FTEwM4uPjMWXKFMTGxuLJJ58EoLecadOmDUaOHIlhw4YhLCwMp06dQmZmJhYvXix4vPz8fOTn5+Ovv/4CABw/fhzh4eGoWbMmoqOjnfJ+ARI+BOGzsIoUlr5PWp0Wy/5Yho+PfSz4uhQUUKBhTENk38gWHROiDEG5zvGESB2nw1st30JMSAz+uvUXlh1f5vAxpWLL/Zkl30ar02LavmlMrS6m75tu4s8jtExGERoPQRkA9Jhzv6pLAVPxcz95vMdsp/r5VKtWDatWrcLbb7+NRYsWITU1FfPnz0ffvpaVZLNnz8aYMWNw9uxZNGnSBBs2bEBwcDAAoHHjxti9ezemTJmCxx57DBzHoW7dunj++edFz/3pp5+a+Oh17NgRALBy5UoMHjxY3jdqhNe0rHAV1LKC8AWk5t1Ya98gd5SnR+0e2HJhi8uWx+Y8Ngc96/SU1O6CpZ+WHLC2ePj06KeSROfIJiMxvOlwk23UW0t+ZGtZcXKDvrrLONE5orpe9Di5lN3b8KuWFQRBsGFPI08xQzuxtgeOsO/KPpfmBPEl23ylEwvx6niMbDrSmdMCAMSFxmFEkxGo1FaatOYwRqvT4rNTn0k67ppTa0yOJfZzvFp6FW/sesPu9hWETCT3BcZmA4N+Avot1/879jiJHidBS10E4UNodVp88ecXdgsVY0M7Z3RCB4CiSrYqJ2MUUOCFR19AjYgayC3Oxdenv2baj/eT0eq0yMrPQuNqjZGZmyk6fmCDgUivmY4msU3w+7XfoQ5UC/Ydk4NgZTCK7xabRHKEIjBHCo6guFJaD8GiyiJDywiWn+P0vdPtal9ByIgywKkl68QDKOJDED7CttxtyFibgblZc+0+hrGhnTN6TkUGR9q1HwcOX57+EglhCcioncG838RWE7Hz0k50+qYThmUOsyp6AKBpXFMUVRSh57qeeGXrK04TPQBQqatE2b0yk23GDUR5sZZ5wfqcxeBFLMvP8VblLbfkPxGEO6CID0H4AI566Qgl2F4tkb9yq21CW2zO3Wz3/nMOzsHGpzZaNecD9NVc8zrOAwC8sesN5uO/uftNt5bm8w1Ep++bjlkHZqGgzLJfEivRIdHIys/ClpwtTOM/O/mZiXEiQfgqJHwIwstxdElKyNBu64WteP/A+7LNkeeXy784tH9+aT6OXT8mas7HM6/jPHSp2QXdv+su6fiOiJ6qqqq4VXHL4cRoDhxuVdyye38FFIhURWLKnimShFNxZTF1VCf8AlrqIggvx9ElKfO+TwsOLcD43eNx5+4dG3tKR46lowWHFqBKUBXM7zTfooeVRq3Bh2kfonvt7lh2fJlDERNW/tnon1iRsQK7ntuFD9M+tJiTK+FF162KW3a9d09pNEsQzoQiPgTh5Thys3qr5VsYUH+ASaTH3h5TriL7RjaGZQ5DWFAYnqz7JKqHV7do27Atdxs+Puq455A1+OXBEU1HANAL0EptJd5r/x4UCgV2X9qNz0595rLSeACIVEWC4zi7EsgB6qhO+AckfAjCy7H3ZhUfGm8ierQ6Ld7b/56cU3MqJXdL8Pmf+oaIcaFxmNx6MgKUAYalP2fDgcOElhOw89JOUX+c1PhUlzpdO7JEFh9qX9NSgvA2SPgQhJdjqxO3GM888oxJIuuRgiM2XYY9lYKyAryx6w18mPYhIlWRLhMa7+57VzC6YuyGvaXfFhwpOIK8O3n412//8ri+ZjyTWk+ixGbCL6AcH4LwcvhO3MCDRGUWakbUNJRMbzq/CV+e+tJZU3QZ0/dOd0o1mhhiS0q8uJlzcA4AoKWmJfo+3BeDGw521dSYUUCBlxu+TO7NfkZaWhrGjh3LNHbXrl1QKBS4deuWQ+esXbu21UbjroKED0F4GcZihXf75TtxS0msvXj7IjLWZmDIliGY+OtEZF60zy/Gk7hVeQs3ym+4exoAhN2wG1dr7MYZCcOBw6oTq8i9mXApN2/exOuvv45HH30UarUaNWvWxOjRo1FUZF9+mhRoqYsgvAhb/ZbSa6QjKz8L43ePt+r2GxYY5vTkX3dxq/wWIoMj7U7wlZv9efsNuTOuyD2ylzkH54i6N1NzU+fjb9f4ypUruHLlCubPn4/k5GTk5uZi+PDhuHLlCr777junnpsiPgThJYj1WzJ2+w1QBqBNYhtMbzcdivv/CVFyr8QVU3YL526dQ8OYhu6ehoGlfyxFxtoMLPtjmctyj6Qi1qsNeOAIzkcGh2wZgoy1GRQhkhF3X+M1a9agRYsWCA8Ph0ajwYABA1BQYGmH8Ntvv6FJkyYICQlB69atcfz4cZPX9+7di44dOyI0NBQ1atTA6NGjUVIi/LcmJSUFa9euRZ8+fVC3bl107twZ77//Pn788Ufcu3fPKe+Th4QPQXgg5stZlfcqRU0KjfNJ+MaU9ix9+Qo7/96JvXl73T0NE66WXpXUXV1umlVrxjRu/5X9JkuoLGKbcAxPuMaVlZWYMWMGjh07hvXr1yMnJweDBw+2GDdhwgTMnz8fWVlZiIuLQ9++fXH37l0AwPHjx5GRkYGnn34af/zxB77++mvs2bMHo0aNYp4H31U9MNC5i1G01EUQHobQclZYYJjVKI3xEzvvvNu1Vld0rN4RXb/r6rXVWoQwrzV+Df85/h/oOB3T+N+v/c40bunxpYb/j1fHo/xeuajYVkBhdXmMsI0113VXXuMhQ4YY/r9OnTpYtGgRWrVqhTt37qBKlSqG16ZOnYpu3boBAFavXo2HHnoI69atw3PPPYd58+ZhwIABhoTpevXqYdGiRejUqROWLFmCkJAQq3O4ceMGZsyYgVdffVX+N2gGRXwIwkPQ6rT49NineGPXGxZPf6xLUzsv7jT5/tj1YyR6fAgFFPqO8/GpzKLHXq6WXrWaJ2VteYxgw5bruquu8e+//44nnngCtWrVQnh4ONLS0gAAFy9eNBnXtm1bw/9HR0fj0UcfxalTpwAAhw8fxqpVq1ClShXDV0ZGBnQ6HXJycqyev7i4GL169UJycjKmTp0q75sTgCI+BOEAfELi1ZKrKKwotHAQZmVb7jaHm1ICwE/nf8L4FuMN56YWBL4FBw4pMSn48k/PsR6gz5j9sF47Z17jkpISdO/eHd27d8eaNWtQrVo1XLx4ERkZGaisrLS5v0KhzyPU6XR49dVXMXr0aIsxNWvWFN3/9u3b6NGjB6pUqYJ169YhKCjI/jfDCAkfgrAToSUpHuNKK5bjONJZ3ZjCikKT5a6Y0BiHj0l4FtsueVZeDbW5sB/Wa+fMa/znn3/i+vXrmD17NmrUqAEAOHTokODY/fv3G0RMYWEhzpw5g/r16wMAUlNTceLECTz88MPM5y4uLkZGRgZUKhU2bNhgczlMLmipiyDsQCwhkedq6VWmxERHO6sLwT8dbsvdhil7psh2XIIwxrDsRm0u7IZ3XRervnTFNa5ZsyaCg4OxePFinD9/Hhs2bMCMGTMEx7777rvYvn07srOzMXjwYMTGxuLJJ58EAEycOBH79u3DyJEjcfToUZw9exYbNmzA66+/Lnis27dvo3v37igpKcHy5ctRXFyM/Px85OfnQ6vVOuvtAiDhQxCSkSJWjCuthHC0s7oQ1dTVbAozgnAE/kY9sdVESmx2AGuu6666xtWqVcOqVavw7bffIjk5GbNnz8b8+fMFx86ePRtjxoxB8+bNkZeXhw0bNiA4OBgA0LhxY+zevRtnz57FY489hmbNmuGdd95BQkKC4LEOHz6MAwcO4Pjx43j44YeRkJBg+Lp06ZLT3i8AKDiO88zGMW6iuLgYkZGRhrI6gjAnKz8LQ7YMsT3wPisyVhiWnszZdH4TJv46UZZ58d3CNz61ET3X9fQo0RMaEIoybZm7p0FIRAEFIlWRCFYGm+SfadQaTGw10e/bXJSXlyMnJwdJSUkOLdMILZvTNRbG2jVnvX9Tjg9BSERqoqG18XKu3XPgMLHVRBy7fsyjRA8AlGvL3T0Fwk6mtp2K9BrpfuUq7Gp413W6xq6BhA9BSESqWLE23t7O6tbwxCobT+1ITohjHnEQi1oS8hCgDKBr7CIox4cgJMKLFVuwJCba21ld7HxzDs5BdEi0Q8ch/Jfw4HB0SOyAt1q+hY1PbaRlFsInIeFDEBLhxQqLUGFJTJSrvQRvdqZQKKxWihAET0RQBEY2GYmBDQYiShWF25W3sefKHszNmoue63pSSwrCJyHhQxB2wIsVsciPRq3BgrQFzE/MXWt1xZZ+W7AiY4XhJmQvN8puGKJIBGGN23dvo/ReKdacWmPh8E39uNihGiHXIce1pqouM6iqi5CCXM7NYsfdnrsdG85twO27t5n35avIrBksmqMOVKN+dH1qP+BnKKCAQqEQbX/BVwpu7reZEm0F0Gq1OHPmDOLi4hATQ2ahruDGjRsoKCjAI488goAA088kVXURhAtwVkJigDIARRVF2HZxG7Po4W9SfE4RXymy7PgyfHzUemfw0nulJHr8EA6c1Sdooea3xAMCAgJQtWpVFBToS/3VarWhhQMhLxzHobS0FAUFBahataqF6JECCR+CcAF8BIe1VFVqGwtrZmffnfnO/okTBDyzUtBT0Gg0AGAQP4RzqVq1quGa2wsJH4JwMkJLTtZ6ebE4QysVSpPliXh1vKDZmTOcoQn/g/pxiaNQKJCQkIC4uDjcvXvX3dPxaYKCghyK9PCQ8CEIJyIWueETR4USoFnEio7T4a2WbyEmJMZqBIme1Al1oBql90oFX2PN8aF+XLYJCAiQ5aZMOB+q6iIIJ2EtcsNvE+rlxSpWYkJi0LNOT7TUtBRdNnPWk3qggp6ZvAVrogcABiUP0gsgAfsDDhwmtJxAic2ET0HChyCchK3IjXHiqDGsYoVlnK3uz/Zyj7sn6/EI1xOvjseCtAUY12KcVR+puVlzqaSd8ClI+BCEE9DqtNh/ZT/T2KslpuLIllgxd4TW6rTIys/CpvObkJWfZRJBMnaGJgieKkFVsOGJDYZl1q61uuKtlm8JjiU/H8LXIOFDEDKzLXcbMtZmYOnxpUzjzZ+orbWxMK/e4s81ZMsQTPx1IoZsGYKMtRkmx+taqysGNxzs4LsifIk7d+8g4/sHnxOtTou5WXMFx1pbliUIb4SED0HICJ/MLKWSqrCi0OKJWqyNBb880bVWV9FzmT+ha3VabMrZ5MC7InyRwopCvLHrDXx67FMcunrIrmVZgvBGKEORIGSCpQzdGnMOzkF6jXRDIilvQCjk/2MrcZpvWMrvTyXthBgfH/0YEUFsLvXmy7KsSPWxIghnQsKHIGTCEYEh5JBr7WbBmjj9xZ9fILco1645Ef5D8d1ipnFzs+YiJDBEUtd2qT5WBOFsSPgQhEzI4ZnDH0PoZhGlikLvOr2RXjOd+clbLG+DIOzhVsUtUf8pIezxsSIIZ+NVOT6XL1/GSy+9hJiYGKjVajRt2hSHDx82vM5xHKZNm4bExESEhoYiLS0NJ06ccOOMCX9CDs+caupqork7hRWF+OzUZxiyZQgJGokodRxanLqDx/ffQotTd6DUUW9me5CS6GyvjxVBOBuvifgUFhaiffv2SE9Px88//4y4uDicO3cOVatWNYyZO3cuFixYgFWrVuGRRx7Be++9h27duuH06dMIDw933+QJv4AvQy8oLZCc58M75DaJbYKe63ra3L+wotCRqfoVXQ4VYdLnedAUPvAeyo8KxOwXE7C9RaQbZ+adsDYuleJjRQ1QCVfiNRGfOXPmoEaNGli5ciVatWqF2rVro0uXLqhbty4AfbRn4cKFmDJlCp5++mmkpKRg9erVKC0txRdffOHm2RP+gL2eOcYl6seuH6NEZBnpcqgICz66hLhCU8PFuMJ7WPDRJXQ5VOSmmXk/tpZ2WZd+qa0K4Wq8Rvhs2LABLVq0wLPPPou4uDg0a9YMy5YtM7yek5OD/Px8dO/e3bBNpVKhU6dO2Lt3rzumTPghXWt1xYimIyTtY1yiTjcB+VDqOEz6PE///+av3f934hf5tOxlJ7aWduV0ICcIOfGapa7z589jyZIlGDduHN5++20cPHgQo0ePhkqlwj/+8Q/k5+cDAOLj4032i4+PR26ueFVLRUUFKioqDN8XF7NVNxCEGDXDazKN6/9of3Sr3c2kWotuAvKRerrEZHnLHCWAhJt3kXq6BIcaVHHdxLwc1saltpZ+qQEq4S68JuKj0+mQmpqKmTNnolmzZnj11VcxbNgwLFmyxGScQmHqdMtxnMU2Y2bNmoXIyEjDV40aNZwyf8J/YBUv3Wp3s2gw6qzeWv5ItSK2fmKs4whL53BrSHEgJwhX4jXCJyEhAcnJySbbGjRogIsXLwIANBoNABgiPzwFBQUWUSBjJk+ejKKiIsPXpUuXZJ454W9I7bVlDPXWko9rkWwBbdZxhOmyLAssDuQE4Wq85je+ffv2OH36tMm2M2fOoFatWgCApKQkaDQaZGZmolmzZgCAyspK7N69G3PmzBE9rkqlgkqlct7ECb+DFy/jdo2DAgqTMD/Lky5/szD38SGkceTRMORHBSKu8J7gE54OwNXoIBx5NMzVU/MqolRReKvlW4gPi7fLcdmaAzlBuAOvET5vvPEG2rVrh5kzZ+K5557DwYMHsXTpUixdqm8EqVAoMHbsWMycORP16tVDvXr1MHPmTKjVagwYMMDNsyd8HXOX5fQa6YLiJSokClNaT7H5pGt8s9ieux1f/PmFXa0wqgRVwZ27dyTv5wvolArMfjEBCz66BB1Mw9u6+//OGaCBTknLikLwIv3/2v6fw5GZAGUAlawTHoOC4zivKWn46aefMHnyZJw9exZJSUkYN24chg0bZnid4zhMnz4d//nPf1BYWIjWrVvj448/RkpKCvM5iouLERkZiaKiIkREsPWvIfwba5b8Ok6H9/a/Z+K7Y49d/9YLWzF+93hJ86qqqop32rwjeT9fQ8jHJy86CHMGaMjHxwoatQYTW000+ZxSzy3Ck2G9f3uV8HEFJHwIKYhZ8psvcZm/BkByjoOQwLLFyw1fxsoTK5nH+ypKHYfU0yWoVnQP1yIDceTRMK+L9Fj7TMl5/IENBiK9ZrqFqKGeW4SnQ8LHTkj4EKxodVpkrM2wKw+HL+Xd3G+zpCdm4yfumNAYjN81HkWV4iZ8zr5ZEs6HF8qDGw6WVcRGBEWYNCcVivDwWBP4gHQRTxDOgPX+7TU5PgThacjdjZ0F41yJrPwsq6KHPw/h3aiD1GiX2A7tEtvhYvFFbL+03eFjjmwyEsMaD7O5bKXVaXHo6iFM2ztNtOeWAgrMOTgH6TXSadmL8ApI+BCEncjZjd1d5yc8n5K7JcjMzURmbiZUAfJUoD4U/pBN0cO6tEo9twhvg4QPQdiJXN3YpcIvd527dc7h8xPeRYW2wvYgBt4/8L5JtZ95ro7Y0pY1SIgT3gIJH4KwEzm6sUu167cnwZkgzDG3OLhaehXjdo3DgrQFSK+RjtkHZ0v+TFO7FcJb8BrnZoLwNFgs+a29JtWun38KJ9FDOAMOHOYcnINDVw9J+oxZcyJ3BK1Oi6z8LGw6vwlZ+VnQ6rSyHp/wXyjiQxAOIOayHK+Ox8RWEwFA9DUpVTBandaup3CCkEJ+aT4O5h1kHm8s4gF9wr0cHj9UOk84ExI+BOEgtiz55bDrd6SCjPBNwgLDUHKvRP4DS7A3Mhb45tYO9goVsfyigtICw3IciR/CEWipiyBkgC8z71mnp0XHdWuvsUKJo4Q5vOgRa4ZrLy3jW1ptsgsAkcGRWNZtGTb32wwAgkuwvFDZlruN+dzWIpv8tjkH59CyF+EQJHwIwgtgTRx9s8WbiAymNgz+gjpQbfHzjg6Jtvt4UaootNS0tJq7poAC09pNQ5vENgAgq1CxFdk0Lp0nCHsh4UMQXgBfQSb2FM4nmL7U4CVMazdN9igA4ZmU3ivFrcpbiFJFYWCDgViRsQKZ/TIRpYqy63gv1H8BAcoAQ+5anDrO5PV4dbzJUpPcQoU1skkRUMIRSPgQhBdgq4KMA4d+9fphy4UtiFRFYn6n+YgLjRM6FOGD3Kq4hTWn1qCoogjBgcHoXae3XcepHVEbgH7JKVIVibGpY/FmizfxSsor+Gfjf2JG+xlIr5FuGC+3UGGNbFLpPOEIlNxMEF6CWAVZZHAkOAWHj499bNjGJ52eLzqPj49+LHQ4wocwbx2RXjMdn536TPJxqqmrWfWKWoqlJknLcgsVW95Y9vpfEYQxFPEhCC+ia62u2NJvC1ZkrMCcx+ZgZNORKKosQlGFac+uq6VXMX73eFy+fRnzHpuHeHW8m2ZMuArjZaUmsU0kLXfxS6WF5YU2vaKMk5ZZl2BZhQqLN5ZU/yuCMIeED0F4GXyVWEbtDHx35jur3j7rz63HhF8noHut7hjZdKQLZ0m4i9UnVqPnup4orChkGs8LigktJ2Bu1lybXlHGScsARIUKP7ZfvX7McwfAnF9EEPai4DiOHNGMYG1rTxDuJis/C0O2DGEerwpQydbrifAdlAolBiUPwmMPPSbp8wQAKzJWoKWmpc1WKvZ4+vA96eQwRCT8A9b7N0V8CMIL0eq02J+3X9I+JHoIIXScDqtOrMLOizsl78snLfNLsGJRRXs8feTwvyIIISi5mSC8DGpUSjiDjTkbJe9jnrT83ZnvBMeZJ18DoGgO4TZI+BCEFyFm508QjsCBw83ym5LaYESpotAktonhe1ZPn2V/LMN3Z7+jPlyE26ClLoLwEqhRKeFs2lVvxzy2sKIQPdf1NCxfsXr1fHzsY1naWxCEvZDwIQgvgRqVEtaQw637uUeek9TyxFiwOGIqSH24CFdCwocgvASy6SesEamKxMsNXzb005IC77fTUtMS09pNY97PWLA0iW1is7mprWNRHy7CFZDwIQgvQU6b/n82+icGNhho176qAJVs8yDkQ6VUYUzqGEEPHI1aYxBFQnDg8HjS44Y+XR+mfchseskLlmPXj9k0H2SBBD7hbCi5mSC8BFt2/lJok9gGLTUtkRqfKrlC7PHaj2P9ufUOnZ+Qn6tlV3Gk4Ai61uqK9BrpolVTK0+sFNx/1YlVSIlNQVRIFCq1lXiv/XtQKBTYnrsdX57+0ub5r5VeQ886PQXbqsSr49HvkX5M7VOoDxfhbEj4EISXwNv5j9s1zu5jmPc64m+Sy44vY+7ptf7ceigVSug4nd3zIJwDHy3hPXCM0eq02JSzSXRfDhwm/DLB5Ocar47HM488w3RuXrCICS9AX+5OfbgId0NLXQThRfB2/lL6MJkj1OtIzH9FDBI9nom1aAlLcrz5z7WgtACfHP0EkcGRkvpxCZkPUh8uwlMg4UMQXkbXWl2x7ZltCAsMk7zv4IaDLbxSqFrM+2FpBmpP7gwfmVEoFAYTQvPzAuyChfpwEZ4ALXURhBcSHBiMwQ0H4+NjbMtTPD/n/IwxqWNMblKUTOob2BIf9ubOcOBwq+IWXmvyGr4/+71F7s7EVhMlCRZbOUgE4WxI+BCElzKs8TCs+XMNiiqKmPfhy4WN8z8uFl90xvQA6CMCZLjoOHxkJa1GGnb/vdtkSYpvMmpLfDiaHP/5qc8xte1URIVEOSxYhHKQCMJV0FIXQXgpAcoATGs7TbJvinGEZ1vuNslRIylEhdifi0Q8IF4dj8ENB2PXpV0WeTgcx2HViVXYemErsvKzsOn8JmTlZ1kYAfI5NvYK0eLKYry5+00UVRRR41DCq6GID0F4MXzOhJSSdH7Jg2+B4Sz+2eifSIpMwuQ9k512Dl9Eo9ZgQssJJpGVJrFN0HNdT0HRwm97c/ebJq87q/8V32iURA/hrZDwIQgvh8+ZyMrPwvjd41FcWWx1fGF5IQDnJzW3SWzjtGP7Kq+kvIJRzUZZiIqs/CybPytzUcS3k+CThuUQusbuyrRURXgrtNRFED5AgDIAbRLbYGrbqTbHvrvvXey/sh95d/KYjh0aECqpgsy4wojPK5Gjj5Q/EB0aLRhJcaQii+9/JafQpYR4wpsh4UMQPgRLTk1RZRGGZQ7DjP0zmI45sulI/KvNvyTNg68wMvZuIWwj5s/kSEUWH6GRU6yQuzLhzZDwIQgfQsrNrVxbzjQuJjQGsaGxTGMVUFh4BfF5SGK9n/iqJG+NDFUNrirbseLDhK+Ro5Gz/Xn7ERMawzQ2LDBMklkhQXgbJHwIwodwxpN4fFg8FAq2Gy4HfYXRttxtJtu71uqKN1u8KbiPjtPhfyf/h55JPR2eqytpGN0QKzJWYNfzu/Bh2oeIDI506HjWBIWjkbOlfyzFlD1TrDow85TcK5HFrJAgPBUSPgThQ8idU8PfjG+U3ZC0H59XwrP1wlZM/HWi1X1+zvkZ8zvNZ+4K7m4uFF8w8bGxlVTOIyQoFFDYFBSOtispKC1AUWWRzXJ2BRSoqqqKaqGmIprclQlfgaq6CMKHMG5kKod54ONJjyNAGSApkmRe+bMtdxvG7x7PtE9USBS29NuCZX8sc6q/kByU3CvBkYIjSI3Td7i3dq2VCiXmdZwHpUIp2Lmc1f24a62u6Fi9I7p+1xWFFYWS5stHcdRBapTcLbE67lbFLSzrtgwBygByVyZ8DhI+BOFj2OPtIwbf4sIe199rpdckl1BfK72GAGUAhjcdjjpV61h0C/c0dl7cCa1Oy9T8MyokCi01LR1u1xAcGIz/a/t/GLdrHADLMnZrcOCsih5jbpbfRM863rX8SBAs0FIXQfggXWt1xZZ+W/Df7v91KPeEj9zYk2NSTV1Ncgm1cWQpKiTKo0UPAHx26jOM3jGaaSyfeC7UuVwIrU4r6sQs1uxTTqhyi/BVSPgQhI8SoAxA64TWmNZumkPH4W/YhpttqPWbrXHlj5QqM/PkXm/xiinTljGNk9ITbVvuNmSszcCQLUMw8deJGLJlCDLWZpgkjfPidkXGCvR/tL/keYtBlVuEr0PChyB8nK61ujpUdWT85N+1VldsfWYrRjYdKTjWvPJHStTAPLnXnREHZ5TVrzm5Blqd1mokB9CLnnG7xllEyngnZmPxw0ePutXuJutcqXKL8GUox4cgvADeeddWXojYOL6txbLjy7Dm5BoUVbJ1dFcqlIYWFzwBygAMbzIcD1d92GaiLktuEJ/4a57cy+/rzLYaYsSr49GtVjd8duoz2Y5ZdLcI//njP/j+7PcW14zvqcXnRIn15FJAIdgry9HO68aMaDqCKrcIn0bBcZxjvyVuYtasWXj77bcxZswYLFy4EIC+S/H06dOxdOlSFBYWonXr1vj444/RsGFD5uMWFxcjMjISRUVFiIiIcNLsCU9Gq+NwMOcmCm6XIy48BK2SohGgdJ+x3rbcbYICw7wBJes4rU4rqWpKAYVoGTOLIOMjGIBwIu4HnT5A99rdBd/3tL3TmEWaHHSr2Q39G/RHalwqjhQcwZAtQ5x+Tj66tCBtASJVkUznXJGxwqJXlq3rHKwMRqWu0uax5zw2h5KaCa+E9f7tlUtdWVlZWLp0KRo3bmyyfe7cuViwYAE++ugjZGVlQaPRoFu3brh9+7abZkp4G5uz89Bhzg70X7YfY746iv7L9qPDnB3YnM3W10puWJc9pCyPAMB3Z7+TNA9zXx4eoURd86Wc9Brpgom4GrUGH6Z9KCp6xu0a51LRAwCZFzNRVFGEAGWAIYribIx7al0tYYtuCeU/iSU8RwZH4rXGryEsiK3fGiU1E76O1y113blzBy+++CKWLVuG9957z7Cd4zgsXLgQU6ZMwdNPPw0AWL16NeLj4/HFF1/g1VdfddeUCS9hc3YeXltzxOJZOb+oHK+tOYIlL6WiR0qCy+bDuuzRsXpHScsjUiutpHTkthZ12tJvi0l0qElsExy7fgybzm8yiRZZe9+20Kg1mNByAqJConC15CrmZs2V5Hdjfq0mtZqEN3a9IXkeUuGvMetcxcQJv6RpHoU7UnAES/5YYvO4UaooSmomfB6vEz4jR45Er1690LVrVxPhk5OTg/z8fHTv/uDpUaVSoVOnTti7d6+o8KmoqEBFRYXh++JiNvdVwrfQ6jhM//Gk4K2WA6AAMP3Hk+iWrHHZspctgcLfLL8+8zXTOF642FstZWs/PkpjLlj4qJPxctm23G3oua6noECKVEVKzuv5Z6N/ok1iG5Oltqz8LLtM/oyvFZ8YPm3fNBRVmEaf1IFqlN4rlcUokidKFWU1V0cBBeLV8VbFCR+FM4b1Z967Tm9KaiZ8Hq8SPl999RWOHDmCrKwsi9fy8/MBAPHxpqHp+Ph45Obmih5z1qxZmD59urwTJbyOgzk3kVck3rSTA5BXVI6DOTfRti5bs0dHYb1ZXSq+JOl49i5lWNtPSlLuzks7rQqklxq8JHludavWtftmL4TxkhMfRcnKz0LW1SyAA1oltEKL+BbYeWmnLEaRPPFh8aLO2470ymL9mafXTJd0XILwRrxG+Fy6dAljxozB1q1bERISIjrOvJkix3FWGyxOnjwZ48aNM3xfXFyMGjVqOD5hwqsouM3WqZx1nByw3qxqRLB9XvnjSa0AYokysEanDl09ZFMgbczZyPBuTIkOibbYxtqNXIi5WXMREhhiiFAFKAPQJrEN2iS2MYzR6rSIVEVibOpYFFYUIkoVhb/v/C2pas78PTSJbYLgwGBB520prS3MYa2QM6/gIwhfxGuSmw8fPoyCggI0b94cgYGBCAwMxO7du7Fo0SIEBgYaIj185IenoKDAIgpkjEqlQkREhMkX4X/EhYuLaXvGyYGthqO80dzzjzzPNI4XLsYuzLb8alijDKzRlYN5B20KpJvlNxGlipLkpTNlzxSLBG5HClYLKwoFk8J5jA0GJ++ZjLlZc7HwyELUiawDVYDKrnPeLL+Jnut6YlvuNhNzwjmPzcF/u/8XM9rPQKW2UtD7xxYBygC81fItm+PmZc2TfGyC8Da8Rvh06dIFx48fx9GjRw1fLVq0wIsvvoijR4+iTp060Gg0yMzMNOxTWVmJ3bt3o127dm6cOeFutDoO+87dwA9HL2PfuRvQ6ixviK2SopEQGSJ6q1UASIjUl7a7CmsCxViQBAcGM40zFi5iFUBKhemfBNaO3MzLZ4xapned3veHs+1wreyahVC5WX6T7WRWEKpms1ZBN373eBSUFdh9PqEqvOzr2ZiwewKGZQ4TdXFmISrEdld3Pr+JIHwZr1nqCg8PR0pKism2sLAwxMTEGLaPHTsWM2fORL169VCvXj3MnDkTarUaAwYMcMeUCQ9gc3Yepv940iR/JyEyBFP7JJtUaAUoFZjaJxmvrTkCBWCyEMPfeqf2SXa5n49Yw1HzZQ/WcebHNq8A4iutpDbQtLV8xi+XtYxviaVYavN46TXTkRqfypw/I1S95mhZtlA1m61cJlb4xGihYyigwPS90zE7UPy9CyWM24I1KuctrUIIwl68Rviw8NZbb6GsrAwjRowwGBhu3boV4eHh7p4a4Qaklqf3SEnAkpdSLYSSRkAouRKxEmVzQcI6zhihCiBbJeuAsHEhS1JuS01L5qqlAGWAyfu5VnYN8w/NF52TuVCRy83YWAhItQIQQ0j08HDgcKvyFmDFa9Cai7MYrEKQfHwIX8drnZudBTk3+wZaHYcOc3ZYrdRKiAzBnomdLaI4nubc7GlY8+oBYPGaRq0xiTqJOQwbOxgLRTE2nd+Eib9OtDk/Y+dha+diFUPGLsmscxBDAQUiVBEWpfGOIFTKL4RWp0XG2gybonNzv81U0k54JT7t3EwQPGL5O7bK0wF9efpHO85abA9QKtC2bgyeaFodbevGkOgxwpZDNACTpNwVGSuwud9mEyEjll9kK5/InoiFtXN90OkDSUnhUubA7y/0vT3l+tZYenypzbwfPkLXvVZ3UdEDUHNSwj+giI8ZFPHxHqzl71Tc02HMV0eZjvOpix2ZvRU+YiC21CMWMRDr58XaeNX8/PZELMTOJTX6xDqHCS0nYG7WXMHIV3qNdKvHsBexOQtF6JQKJXSczmJu1JyU8GZY798kfMwg4eMdiOXv8M/YY7s+gg+3nWE6ltiSF2FKVn6W5AaarI1TWbF3mczWMW0tz9kzB2vCzlZDUXsxF39ibtr8Ut/ABgP1ieSMSewE4cmQ8LETEj6ej638HQX0CckcxyG/uEJwjDlfDmsj2ZHZ33KBpObYWLvpAuwixVxAFJYXikZT7I1YSIk+8d3tPzv1GYorH7S4kToHQVEYGo9yXTmKK4odEkQrMlYgNS7VrggdQXgrrPdvn6rqIvwD1vYSb3Sthw+3WebwCCHVkZm1TF4uPEFkScmxYSn7nrZvGsKDw9EivoXojVcsYvRm8zdxrfwaLhVfQo2IGnj+kecRHBhsx7vSI1TdxjqfyOBIvJT8EoY1GiZJQIhV4fEtPRzpAXat9BqzmzZL81mC8CVI+BBeB6tIqR0bhjcYl7ykODK7uou7q0WWGKxePXw3cFtl30UVRXhl6yuiS19iEaOrpVcx4dcJJttWn1ht9/IZK2LzKa4sxidHP8HDVR+WfH4hwSXmyRQdEo3m8c2RmZtpfhgLqqmrkW8PQYhAVV2E1yGlvcSozg9DEyHeQkCqI7OtLu6Avou7kDu0PfAiyzzCxYuszdl5spyHBVYn6QBlgKSbqZBbsbWIEesx5IQlgiXk8mwv5i0rVmSswI5nd2Bex3nMlWjk20MQwpDwIbwOKe0lApQKTOvbEApYdkuwx5FZShd3R3G1yGKBtRRdys1USDhINQp0hvgwRsqykSNodVpk5Wdh0/lNOFJwBKlxqehZpydaaloiQBkgSXyy9nqz1nyWIHwRWuoivA6p7SXkdGRmXWbLu1WG5b+eR+7NUtSKVmNg29oIDpT2nCFFZElNzHYEFodoqa7J5vkm9iy/ODNnxdFlI5bkadYKOFvtSdJrpCMrPwvXSq/hmXrP4ONjH1t106bEZsLfIOFDeCVSxUyPlAR0S9Y4nCDMusw27ttjJt+/t/EUXnmsNqb0ash8LlaRJTUxm0eqj44x5rkpfKSCpYWFNXjh4MjyizNyVhxZNmIRNGL5Q2I9uawlRptXckWqIgEOKKp84BZtrYcbQfg6JHx8EE+oAHIFxmImv6gMN0sqEV1FhcjQYGh1nMV75h2ZHYFfZssvKpdUb8MBWPbrBVy4UYpl/2CLRkjJZZKKI/46LOXl/LGEIhPW4IWDI322nJGzIiWx2xgWQZNeI91q/pBYTy5z8SmafF2hL7kf2XQkaobXlCxyCcLXIOHjY3hKBRArQiINALNwC1AqUFRWiblbTrvkPVtbZmMh82QBfjx2BX2aJNoca0tk8X5FrInZPFKjC+b7sggZ42Nt6bcFWflZGL97vInvjel7MRUOfC6LlIiRmPiQA2vzEVs2spUQzQuaKkFVHC47ZznX2jNrybOHIEDJzT6FJ1UAsbA5Ow8d5uxA/2X7Mearo+i/bD+av5eJ5u9lmmzrMGeH6Nwdec9ifb5swS+zaSJNIy2sMbUp644znYsXWWKiB5CWmA04Vp0k1qdLCONjAUCbxDaY3m46FPf/M30vwsJBLJFaCFfkrEjtMcaaEJ11NYvp/NaW8FyVfE0QvgBFfHwEWxVACugrgLolazxi2UvMC+dW6V2LsWL+OI68Z0cjY+Y5Q9dvV2DGxlM29wOA4vJ7khKSq6qDLK5LpDoIs59uJDmiZa+pndTycqFj2UrKFYoyCeWyiC2tuSJnhSWxm4c514jxklpbwiPPHoJgh4SPj+CpFUBCWBMsQoiJGHvfs1QDQrGcKeOcoR+OXmZ8N3pYEpLF5gkARQICkQV7b5BSy8vFjiVFOPAImfx1qdnF7sRsR2F1eWbNNWqV0Ao/nPtBcv6QPecizx6CIOHjMzi7AkhObAkWIYREDOt7yTyZb9iHJUo0ZV02yiq10ESGorCkEjM22o4MSU0wtjW+8p4Ob687blUc2hPBs/cG6UikwPxYrMLBGnIcw9mwJkS3iG8hOX/I3nORZw9BUI6Pz+DMCiB7EcuhcUR8Ge/L+l5W/HbBkOvDEiW6UVKJN745hv7L9mPEF2z5Q3wiMgu2nKI3Z+ehzaztuFkiHtWx1yjRXlM7eyMF/myQJ8VsUGr+kCPnIgh/hyI+PoKzKoDsxVoOjSPi6+zVO9h37gZaJUUb3rOt6JHxMpkcES+hpTfjai9rURoFrCckb/ojDyO+YE9A3XpCL75YrQvsqU4C7C8v9/ebrZS8JnuWAe09F0H4MwqO41znd+8FsLa190T4nBBA2M1Y7uaZtuYh9sH66IWmGP31UTjSaYEXUQAwfA2bUPhyWBsAQP9l++0/scAxzfOHzAUfj/ESmVDe0JbsPIz68ndZroutn7NQWbpGrbF6gxQrgxdCqVBiXsd56F67u7Q34KM4YhbpyeciCE+C9f5NwscMbxY+gPt9fLQ6Dh3m7LAahQlTBaCkwrF+SsZiLivnJpb/dsHmPv9+oSkeT0lA/Xd+dkhcGDMq/WHUi69iEm3hRY2xqaIm4sHrQj8jocote5Aicu25QbL6+HzQ6QMSPQRBuBQSPnbi7cIHcK9z875zN2SNqFiDX76b/2wTvPjfAzbHOyPiY0zV0CC83L42RnWuJ3q9bUXD5IC/LnsmdnbKz50XTDsv7sRP539CYUWh4TVbUSOCIAhnwXr/phwfH0SO1gxi2BJVclaNPdk0EeuPXhF9nU/wBQfm/Kaf/hA/nqPcKruLD7edxcq9FwQ9dqSW8duLs60L+IqqlpqWGN9iPC2rEAThVZDwIZhhWUaTo2qMFyqJVUOZxl8vqWDu1h4bpnJ4fra4VXoXr605go8HpCIqLNggEnUcJ7mM3xF+vl91JhTxkysq6A1l5QDlvRAE8QBa6jLDF5a6nIHYEo15TolWx6H5jEzcKmPLVxESKhyk5bzwCcYswuy3v64zLYvJgVIBk1widXAASisdy22yB/Nr4Oo8MHeLDkcashIE4T1Qjo+dkPCxxFbCsnFOCQBM/O4Yvjti28n48ZR4HMgpxM2SSsO2KHUQCiUk+Uapg3DoX90M0QpbkYwfjl7GmK+OMh/fFzAWpwCYBKxcuFt0iFWi8aX7LB45BMGETgvk7gXuXAWqxAO12gEUVXQplONDyAZra4iPdpzFV1mXmJdzfs7W3wyjw4LwVNPq6NwgHuO/OSppbuY3cFv5Ta40cPQU+Gs0/ceT4DjhYnR+29vrjqPsrs6kCs1eHOkCLwes3dHTa6TTshfhGCc3AJsnAsVGOYQRiUCPOUByX/fNixCEnJsJm7AmLH+47axdOSyFJXex4rcLOHShEPnFFZL2vVV6V5J7MW966P42rY4xtks9VFUHSdonr6jc5vW9WXIXb3x9FP2X7UeHOTusdre3hi3RwYHDu/vexU/nfkJWfpZgN3hHoY7lhEs4uQH45h+mogcAivP0209ucM+8CFFI+BA2cXaUhL81rtybY9f+UirJeIdlb0YB4LP9ubL4/lhDqDUHKyxNTQsrCjF5z2QM2TIEGWszsC13m71TFYQ6lhNOR6fVR3qsxVE3T9KPIzwGEj6ETVwRJeEAu2/krMKM7x1WcU+HMV3qwUXWRrLD9xNzxXkA/RKZVqLjo1QxwS9/ySl+qGM54XRy91pGekzggOLL+nGExyApx+eTTz7B999/j+joaAwfPhydO3c2vHb9+nW0atUK58+fl32ShHsx7kMlVoUlF1VDg5grwgB95VShgAgwT3IW6rJO2MZeTyCpYsIZOTfUsZxwOnesRzUljyNcAnPEZ9GiRZgwYQLq168PlUqFnj17YtasWYbXtVotcnNznTJJwv30SEnAkpdSoTHrQK6JDMEbXR+R7Twd6sVKGq/jgJFf6Jdj+IjOjB9PoOX7mei/bD/GfHVUtMu6txMeEuiyXCWpxpS2usALIXfODXUsJ5xOlXh5xxEugTni85///AfLli3DgAEDAAAjRozAk08+ibKyMrz77rtOmyDhOfRISUC3ZI1FuTgAfJV1UdQ5mQUFgPgIFQ5dKLQ5VojJ3x/HtA0nJCdHezO3y++57FxS87ysdYG3hVw5N1qdFpGqSLzU4CWL1hrUsZyQhVrt9NVbxXkQjn0r9K/XaufqmRFWYBY+OTk5aNfuwQ+vbdu22LFjB7p06YK7d+9i7Nixzpgf4WGIlYuLLYWxwD+L929VEx9uOyt5ThwgyfuHYMe43YdUutbqigVpC5iamhojR86NkH9QlCoKvev0RnrNdHJuJuRBGaAvWf/mHxC2YwXQYzb5+XgYzEtdsbGxuHTpksm2hg0bYseOHVi5ciUmTJgg++QI76FHSgIWv9AUVUKk/4JrIkOw5KVU1I4Nc8LM/IOQQPnrFMzbfdhD11pdsaXfFqzIWIFZHWYhShUluvylgAIatcbhnBveP8hcbN2quIU1p9agqKKIRA8hH8l9gef+B0SYGX9GJOq3k4+Px8Ec8enQoQPWrl2Lxx57zGR7cnIytm/fjvT0dNknR3gPszadxLJfcyCl+OfJpol4KEqNtnVj0KZOjCQ/HsKU8ns62Y+pkamNhXE/r5DAEMHlL7lybsi0kHALyX2B+r3IudlLYBY+r732Gs6dOyf4WsOGDbFz50589913sk2M8B5mbTqJ//wi3YOH77z+0c6/kBAZgnd6NZDUo4twLkPa10a3ZI2sxxRb/pIr50aKaaE3NFclvAhlAJD0mO1xhNthFj6dOnVCs2bNUFZWhgEDBiAyMtLk9YYNG6Jhw4ayT5DwbCrv6bDsV/uMB43JLyrHiC9+l2FGhFy8v+lPrPjtgl1RH2s907rW6or0GulOaVxKpoUEQdiCWfj89ttvWLlyJSZNmoTx48fj6aefxtChQ2mJy8/5bN8FSctbYlCnXM+Ed2+W0ryUpfu78fKXnJBpIUEQtmDOiGzbti2WLl2K/Px8LFmyBH///Te6du2KunXr4v3338fff//tzHkSHkruzVJ3T4FwIlLdmzdn5+G1NZaeSY60v5CCLf8guRKoCYLwXiSXgoSGhmLQoEHYtWsXzpw5g/79++M///kPkpKS0LNnT2fMkXAhvAngD0cvY9+5GzZvdrWi1S6aGeEIYcFKRNhRcQeYujdbQ6vj9B3gRY4B2Nf+QgpkWkgQhC0cqoGtW7cuJk2ahClTpiAiIgJbtmyRa16EG9icnYcOc3aYOB7b6tA9sG1tKBgrnQe2qSnTTAmplFTqUFxu2iixamiQpA7v+cXW3ZsP5ty06o7NKqAchU+gjlPHmWyPV8djQdoCMi0kCD9HUq8uY3bv3o0VK1Zg7dq1CAgIwHPPPYehQ4fKOTfChfBLFObP4rZyPIIDlXilQ5LNBOeEyBC807shtp0qcMjhmZCPorK74AA8k1od3x25bHP8jJ9OIDRIKZrrw9rWQmr7C3twZgI1QRDejaSIz6VLlzBjxgzUrVsX6enpOHfuHBYvXowrV65g2bJlaNOmjbPmiVmzZqFly5YIDw9HXFwcnnzySZw+fdpkDMdxmDZtGhITExEaGoq0tDScOHHCaXPyFRxdopjSKxndkuMEXwP0RnhT+yQjOFCJqX2SSfR4CBz0P5s9f12HJsJ2S4qbJXet5umwtrWQ2v7CXvgE6p51eqKlpiWJHoIgAEgQPt26dUNSUhI++eQTPPPMMzh16hT27NmDl19+GWFhznfc3b17N0aOHIn9+/cjMzMT9+7dQ/fu3VFSUmIYM3fuXCxYsAAfffQRsrKyoNFo0K1bN9y+fdvp8/Nm5FiiWPaPlvjohaYIN8sjqRoahLFd68nuB0PIAwcgv7gCdyrYvJM4ANM2nBAUwa2SopEQGSLallQBfeTPnvYXPodOC+T8Chz/Tv+vTmt7H184N0F4AAqO45gewPv27YuhQ4eid+/eCAhw/5PTtWvXEBcXh927d6Njx47gOA6JiYkYO3YsJk6cCACoqKhAfHw85syZg1dffZXpuMXFxYiMjERRUREiIiKc+RY8hh+OXsaYr47aHPfvF5riiabVrY7R6jh8tOMvrPwtB7fKHtxM9QaFyZix8aRVkWVPry9PIEodhOdaPISvs/42ed/OpFcjDeLCVVi5N9cl5zPmmdSHMOeZxhatLPglU0Cwa5Gksnif5eQGYPNEoPjKg20RifqeT85ub+DOc3s6Oi05L3s5rPdv5ojPhg0b8MQTT3iE6AGAoqIiAEB0tP7pMScnB/n5+ejevbthjEqlQqdOnbB3717R41RUVKC4uNjky9+Qc4ki82Q+Fm47Y3Hz1xsUWpY5m+ONogfQN0kNDQrEv59r6rJz/nbuBtYdvWJ7oBP47sjfaP5epsWyV4+UBCx5KRWaSNPPCt+PjUTPBn1Dy2Kzn1txnn77yQ2+eW5P5+QGYGEKsLo3sHao/t+FKf59TXwY5oiPJ8FxHJ544gkUFhbi119/BQDs3bsX7du3x+XLl5GYmGgY+89//hO5ubmiFWfTpk3D9OnTLbb7U8RHq+PQYc4O0aRjvkP3nomdrTar5I9jS9z4Mt4asbIXBYSjONacm/0WnVZ/MzUXHgYU+ujL2OPyRxrceW5PhxeEFr+59z+v1GjUa5A94uNJjBo1Cn/88Qe+/PJLi9cUZrXVHMdZbDNm8uTJKCoqMnyZd6D3ZKR67ogRoFRgap9kALDIz5DSodtWrpA/wPITeLldLXSoG+P0ubgCDsKJ7wFKBdrWjcETTaujbd0YEj2AfhlFVHgAAAcUX9aP86VzezI6rX7pz1ppx+ZJlAflY9hdzu4uXn/9dWzYsAG//PILHnroIcN2jUafPJufn4+EhAdPnwUFBYiPjxc9nkqlgkqlct6EnQRLWwAp8EsU5seU0qHbFWXK3k635DhsPnHVpwQin/je1kzMUdTHjDvizVPtGuct5/ZkpAhCakDqM3iN8OE4Dq+//jrWrVuHXbt2ISkpyeT1pKQkaDQaZGZmolmzZgCAyspK7N69G3PmzHHHlJ2GVM8d1htQj5QEdEvWiI61dRxXlSl7M5knC9w9BadgLnrlFuY+QRXxBzC7xnnLueVAzsRj42MV/Mm2j78JQh/Ha4TPyJEj8cUXX+CHH35AeHg48vPzAQCRkZEIDQ2FQqHA2LFjMXPmTNSrVw/16tXDzJkzoVarMWDAADfPXj5YPHfeXnccZXd10ESEoLCkAjM2nmK+AfFLFOaw3Mj4cmYyKPRcwoKVKKnUyX5cY9Frrxmmz1OrnT6PpjgPwksr9/NsarXzrXM7ipyVaELHYsFTBSFhF16T3CyWp7Ny5UoMHjwYgD4qNH36dPznP/9BYWEhWrdujY8//hgpKSnM5/H0cvZ9526g/7L9Dh1Dammx2I1M6Dhi5cyE99GlfjVs//Oa1THmie+2EtxZE+V9FkMiLSBY8O/MRFp3ntteRBOPAUAhbc5WjyWGHyd9eyE+l9zMcZzgFy96AL04mjZtGvLy8lBeXo7du3dLEj3egBx5NFIaRkp1dRYrZ3Y2z6Ra9xcipDOkfR18+lIqqoYKB4aFEt89pV+Xx5LcV3+zjjB74IhIdL7wcOe57cFq4jH0238cw5Z4bPNYQtz/hPeYTaLHx/CapS5Cj1x5NMY3IOOlLfM8Hp2OY7qRrfotB4PbJyFAqTDkCq36LQczNp6SZb62yDxJa/Cyc//vfkhQIFB2z+Jl88R3rY7Db39dZzq0XyfCJ/cF6vdyj1meO88tFZuJxwDKbgK/zAfSJjp+LHMiEvWix9MEIeEwJHy8DLnzaIxvQJuz8zBtw0mTLtyRoWzdu2dsPIX/7skx3AgDlArEhruuWq6o3PLGTDjGmv252JydL/o5e6dXA5MlTvMcMGv4fSK8MsB9VULuPDcLfPLxKUbzwANLgI5vWhdvrMnJHScA1ep7tiAkHMZrlroIPdY8d+yBvwFtzs7D8DVHTEQPoO/gzUpeUTmGrzmCTX/kmRyb8E5+tiJ6FNCLXa2OM+R1sYgesX5dcnlSEV6OsYPywaVs+5QV2vYfYk1OTuoENHpGLwxdKXqof5pLoYiPFyLmuSMFPsm0VVI0tDoOk74/Ltv8Rn55BIMv1ELXBhpoIkJwtZiqvHwNfolzQeZpfHnwItPPV8wMUyhapIlQoX+rmqgdG0YeQP6CXcnH9+GjQ2JRGnuq2lzVu4v6p7kcr6nqchWeXtVlDJ+Pk19UhhkbT6GwpFLSDYivxvrtr+t48b8HnDLHquog3Cq963etHAhLhGwUxCoGWfYlfAibLTUYsSYYpFS1uUqMULsMWfG5qi5CHKVSgUFtawNgW/4ybxi579wNp82tqFS/VBapZssVInyTUel1sWdiZwtjTbGKQXN4DyDzpqiEB+HIco09ycdCWGu4ylrV5qpmrtQuw23QUpeXIrQ8UPW+uLhV+iAvJyEyBO/0SkZUWLCo4/K5a7edNk/+VzokUImRaXXx8a5zTjsX4bm0f7iaxVKVlN5uHPSifvqPJ9EtWUPLXq6CdbnH0QiJbM7I9z8pmyfpq9fM52qrqs2mGLFybKlQuwy3QcLHCxFbHigqvQsOwBtd6zHnRmh1HPafd76nSn5xBfKLy5x+HsKz4HPJmteKwr5zN0zEt9SSdjELBoIRqTkrrGJGbLmGj5CwLNfI6oxsQzBYq2pzpRih/mlug4SPl2HLUFAB4KusS8zOuAdzbqKwlL1yyxHWHrkCVYACFVrK9vEXOAB9mySg07ydFu1OXmhZ065j+rUHkL1Ijciwihl7IiRCAow1+fiJT4DTG9kqvuwRDK4UI97eP82LoRwfL0NuZ1xX30RI9PgXnetXw9Jfciw+s/lF5Vi47QyqqoMk2zJcv11B5e5SkJqzIiX3REqEhJ8LX66+dqj+34UpwJ8b9SIMgGWmopGDct00oAFjsq89guEG41K8HGKEF3uivwEKIKK6Z/ZP83JI+HgZrEKFdRx57RDO5OilIqvtTvhqPyniZ8bGU+gwZwclOrNgU8RwwI+jgfO7HyTRShEzUiIktgQYwJZ8bFMwAAiNAjidtMRgnRY4vNL2OLnEiDKATeyRiaLskPDxMliFCuu45rWiEB0W7MiUCB+kVe0oh/ZXAIgOC8LNkkrRMRyAwtK7eKNrPcm93fy6yktK9RRT24dC4H999ZGXXXPYHZNzdgPX/mQbG1aNLYpUvxcwNhsY9BPQb7n+37HHTZfjrAoGgffEWoWVuxe4zfB5Sh0knxjxtv5pPgLl+HgZtlpWKADER6ig4zj8cPSy1QRnvjLM2s2J8D+qqAJx8EKh3fvzn7SnmlbH8t8u2BxfOzYMeyZ2xkc7/sLK33Jwi8Et3G+rvKTm6kjJRSm+AuyayT7+l3kMg+7n5nCctKRhW4nDvGAwvxbmSEmwZr1WMXXZxrHiTf3TfASK+HgZ1lpW8MsG5fd0ePG/BzDmq6Pov2y/4LIAS5sBTYTKrhwMX0Wp06LNxT/Q9+RutLn4B5Q+6q9xp4K979njKRpEh5l6NPE+UV2TNUzHiAsPQebJfCzcdoZJ9PD4Xad3e/xl3JoYa7RcU8rWvFaSUEvuq48ODfxBv7QliAQ/HHcmG/OVZu5ol+GHUMTHCxFrWRF53yX5llmVFr8swJsWshjHRYcF4Ze3OmPHn1fx2pojss5f7y3UADM2nrK75YaryTi9F1O3L0Xi7Qd/wK+Ex2J6l39iy6P+mXwYEqREtwbxGNCqJv7ML8alwjLUilZjYNvaCA5UQqvjbEYn+VL3TvN22u3s7RdVXvb6y9islnIixt3Nc35l20eqqFAG6L/KrEUoGUvQ7WlrQXglJHy8lB4pCeiWrMHBnJsouF2O2DAVxn97DIDlE7P5sgCLcdzNkrs4nFtoEFlvrzuOmyXylL0/aD2gwL9+yPb4pbaM03uxZL3lEoDm9nUsWT8Trz35tl+Kn/K7Ooz79pjF9v/uyTH8jKf2ScZra45YtCwx7tt1OLfQIQHsFwn69vrL8Pkw3/wDsPgpOInHJgB1Opku10gRFVL9huQqQbd5rThKNvYRaKnLRTij+3SAUoG2dWPwRNPqUCoVFp3VjTFeFpBaGdYjJQHv9G7o8HwBYEj72uiRkoDN2XkY+cURjxc9Sp0WU7frPUPMf1n476duX+qzy172YJx4zAtn8+Rl47Yp9kZsxDq9+ySO3NzFEmilUL+3XtCwEFffcrmGtYLp1I/A/Ecsy92tJSjLuUTFX6vQqpavhfrB58xPoIiPCxBqLyF300UpYsaeyjBNhDxP1d2SNZJ6NLmbVn+fMFneMkcJIPH2dbT6+wT212zsuol5MOYRRvPopHnCvT0RG7FO7z6Lozd3PoE251fgu8E2loYEUAToozi/MiQ0W5uDUEIyvyT2dxawd5HlfsVXrCcoO2OJquyWwLZC9kRpwqOhiI+TEUsilrscN7aKinkcXxlmxTbL4km6VVK0Q+KHP2bzWlFY9ZuloZ2nEneH7QbBOs5TiQ4LRmSIfM9B5onHxtHJtnVjTMSKrc8jAJhrG/NGuz4Pi3eNOhao0Vr8dWWA3gCwz6L7x5EgGE/9AJTcYDPc02nFS+35hGTzcnWdTlj0GODEE5Tl9MNh8j0aA5zbRc1DvRgSPk7EVnsJQP9ULIsLLeshONuVYYDlk3TmyXyU37X/F924dcGMjafsPo6rKajC5mfDOs5TuVlSiaJy9mouVgpul9tc5rX1eVQA+Kh/Kr4c1gb/fqEpvhzWxqLTu8/D4l1Teh1Y1MS2b429S1+bxgMNnoCw5eT9nJi7pcBnT4gvUwnl7wDApnG2z2/sAM36nqT64TD5Ht3Uv8d5dfS+R8YCyJEO9YTLoKUuJyKlvYSjTRevl1RIGsfnXkzbcNIkN0gjsAQn1hRVCr0bJeA/v+Q4cAT3cPChhrgSHgvN7euCTwk6APnhsTj4kDw5UL7Gheul6DBnh81lXrFKRaHPo9/C4l3D6ltj7B1zehOw/xPb5y+9DhxYov9/hULvzcMTGqUXBOZLaMbzAYSXuVIHA6U3bJ8fsJ7rJIcfjpRy+rJbet+jA58Cff6t3+ZIh3rCZZDwcSJyt5ewhr2OzhynM/lepzP9Xq58nI3HvdNhV6cMwPQu/8SS9TOhg2mIlL9S07v8Ezqq9DBBAb29wsJtZyw+O+b2Cjy2coEI6G+gj/QAFtQXEQtWStuFoi1Jj+n/vZQFXM5inwf/d6PNCP181r0qNlA/nx/H3BdFAk1PpZgm2sp1Euq8LqVKzB6PnrKbwDcDhV+TYqBIuAwSPk5E7vYS1mBxdNYY5e1szs7DcAF/nqu3KzF8zRF8ev+mxFL6zoI3JDKLseXRdnjtybctfHzy/dzHRwzjQmBry7xT1mWjc/14BAc+kJN8LhBhhUsHbERIBErbxRyfU54Bsr+zvbwjiAI4+QNQL8NGqwdOLw7EXmNFHSvdQ0eq07XsvkdWhCjhNijHx4nYk0RsL1LydrQ6DpO+P271eJO+Pw6tjvMPczgGtjzaDh2GL8cL/WdidJ8JeKH/THQYvpxEjwCayBC80bWehZGmOTdKKtFm1jb/7LflCFJL20Udn6/oE4rtEj3AA4G1x879JdLrA2nC4eQGfSRGitM1Sy6VZDjr+UmEyyHh40TsSSJ2BBbPFADYf+6GzZvSrdK72H/uhn+YwzGiUwZgf83G2JDcCftrNvaL5a2BbWpicLtaAMRvA2O71MPnQ1ubJB7Xjg1jOv7Nkrv+22zUXqSUtlutUpKJa2ecd2yedqOBhk+yj9dp9V3nBbHRxiK5L/Do45D9mknJHyKcCi11ORlXJ22y5EnsO8/WN2ff+et4o9ujVpfQCN/ms/0XkRAZglc7JmHDsTxmLyqpgtnjm41KdRN25rGl+NawVCk5yqn7vbLKbonMh5G0t4Ejq0znq44Fen4ApDwp7Vi/zGdrY3HgU6D1cNPrvfUdfcK33Li1bxphDAkfF+DqpE3beRKs51UYolZCbQcI/yC/qBxLf8nBxwNSERUWbPEZ1uo4i8+2rZwzY+SsbnQKUvNEnH1sq60VzHxrXBJl4E0HOFjOh3H/iESg45v6L1si0JZQ1GkfVJ/ZYsvbwL6PHlzve5XA3sUS58+AIsC6xxLhUkj4uAhPStpsWzcGH+38i2kcIB61SogMweMpGqz47YKzpkp4APztbMbGk9gzsbOJYLfmSs4LZlY8Mp+Mz48RqkZytFpH9Ng2nIoB2y7I/H4uiTLcT14Witiw7p86SP+/QlVZxrAIxdy90pypjX+WpzfDKY93nFaflG7tvREuQ8FxHD3EG1FcXIzIyEgUFRUhIiLC3dNxClodh+bvZVrN84lSB+HQv7qZPNHnF5XhZkkloquooInQP9kfzLmJ/sv2u3D2hDv5clgbgyAW83fiZdGSl1IBAG+vY2tEa3xsj0Cn1Rvwid7I70cqxh6Xvuxl89jQuyDbOjZL9GNhimNVSspAQMdgbtlvOdDwKf18zu9ma29hTGhVoPUIfdRH6D2LCUX+E/fc//SVUzveB/Z8IO3cUABVNHrBpJUowEOjrVStGdFvOdDoGYnzIqTAev+miI8fEqBUYPbTjQTL2XlmPd0IAUqF1Sf6AKUCrZKiUTU0CLfK5OncTng2fFRGq+MwbYO4Kznfq2vPxM7oXD8ebWZtw80S4c+IudWCx2BvR3RZjg22Y7P41mTMAr4dDLu7sweGApW3bY+rEv9gPlbL20XgDQH3/htoNwbo8IY+SnLnKhBWDfj5LYgbJNz3CrJm7mgVDrgjYc4tXwFqtn2QQP7ZE7b3MY6+OTNnjLAJCR8/pUdKAj4VcG42TlgVe6LPKyrH8DVH8MmAZujZOBEvt0/Ch9tcUNlBuB2+J9xHO86afG7MMc/bmflUI8Oyl0BGionVgscYGDrSEV0M/oZ3cj3beKkCQmwpqN3rln49EdWBhCbAmc0PDAmNiagORCWxlasHh+vfm04L/LkR2DxZ2ryNqSzRC6BdsyCpF0/ZTaDM/tNKImMWEBis/3+dVlqTVGfmjHk6HiL4SPj4EeY3lW7JGtGkaxbH5lFf/o6PoMCozg9j5d4cmyXyhPdz8koRdpy6iuWMeV15t8qw/NfzyL1Zin6pD+HXs9dw9faD9ioaM6EtFl10S8sKRzuimyN0w7NFyTXLbWI3D2v5SHsXA8+sAsJiHuxXeuN+JEjkt/zOVX3UiYXK2/qoB+uyDxMenIVxZvMDkSIl2dyZOWOejgcJPsrxMcNXc3yk3lT2nbvBnLvz6Uup+P1ioVf24iKci/ltQKkAejZKQLfkeBOhzZIv5HLxYzM/RkKOj2h+ig2eXgY0fs70OEI3j+6zgK2T2fORdFpg3sMyihR/QuTnLvizqf4g2dyZOWOeDkt+lgzih/X+TQaGfgB/UzFvPcH3TBIyj5NSYTP5++MkeghBzP/M6Tjgpz/ycPJKEdrWjTFEF6dtOGG1vcX0H09adHV3OlZdfM2e5K3hiIlguJHYE3VgzgO+G8SejwTc97kh0WMfIk7MyX2BsdnAoJ/0icyDftKLGF70HPhU2s/IV7D6+bdhJukkSPj4ONaWrKzdVKQY0BXSEhchkWW/5qDynj6v5KMdfyG/uEJ0rHG+kMvhy8YjzKJNEYnsT6n2mghGVH+QF8Jy82DhztX7N2GGbuyEdYRyu/jk7kbP6P/ll7cWpug9g1jI2e1SEeB0pBQJuAjK8fFxbDUZFTOPa14rCkqF/gmdIORGxwGf7buA6lGhzInxbvP5Se6rL5O2NynTXhNB42iSXA7MVeLv+9zccvxY/g5Lbpc9S5y/zAOOfu47yc7OKBJwEBI+Pg7rzcJ83OHcQhI9hFPJuVGC/+5hXyIVjEK6qkrElrGeNewxEUx+AtBWAjm/6t+TwzcFo8qiE+scPJa/Y1alBQh/DgH7lzh9KdlZ7iIBGSDh48NodRyu3xZfQjDG/KbikS66hE+hAKxGI41JEPL5cXaViFyiymZvLQFO/qD/AvT7NvuHhBPaqCxSx0o4FiGIcTRO6HOojgVqt3cgSnffm2jzJH200ZuTnaX0lnMRlOPjo2zOzkOHOTswY+Mpq+MUEL6pUFd2wpkoFUCzGlHM43mfHwPWEn2/+Yf+dUfg8zJW9wbWDtX/uzDFvuNaTZJmoPgKsHu2jUEKfU7Qs6tt5yMpPLQRrLcQWvXB/4t9DkuvPxCudiMx90Wn1UcIj3+n/9dT8oTkKhKQEYr4+CBipcHmmJvHGSOlySRBSGXYY0lIqBrKNPaNrvVMS9ltJvo6+KTsDK8Vsd5asmB080juCzToI7zskvOrftu1P2U+v8ic1DFAo2eBwyuBez4UQS67pf8cPLNKbyHg7L+QLMucHuSRIwhrbzkXQT4+ZniKj4+9DrZaHYcOc3YwLSHYModjFVAEIUSX+tWw8/Q1k1wxpUIveib3TDZ8Vq2J64TIEIvGqMj5VR+BscWgn6Tn5Tjba4VfPju9Cfjja72JoKOEJwKPW7nB2WOcSNjgvrArve78U2XM1AtYseVWF3nkyIKTc/KoV5cHIVXEOOJga6uKi+edXg0wsG1tHM4txA9HL4vOK1IdZOHIHKUOwvtPNsLb64+TW7Of0S+1OhIiQ/HRzr9sjn3lsbpY8lILfLbvAnJvlqJWtBoD29ZGcKDS8DvxeIoGK367IJaVIhiNdGqViDP7cwEPkqSTHgO6v/fgJnD1pB2NNe9Tp5M+uiWEvcaJhA0414gehdK0DN48iuPs6KfcOFIkICM+KXw++eQTzJs3D3l5eWjYsCEWLlyIxx5zz8WWKmLEoiy82aAtB1vWpOQrt8rQad5O0XlZi/aQb49/EhkaiLnPNAEArD3yt2ikxrjpaIBSgaGP1TF5Xeh3QqEAjGPPGmtC35lVIq4sveVvAjotcHGf/cc59iVwfhfw+FzTJ3tHjBPdSUCwvqKNsOyhZr7c6myh7qP4XHLz119/jbFjx2LKlCn4/fff8dhjj+Hxxx/HxYsXXT4XqY7J9poNGsOalLz8twui89r0xxWrfboUAN75IZuiPX7GnH6NEaBUIECpwNQ+yQBEUxWFIzUQ/53gP9JD29fGl8PaYM/EzuICn68SEU0UVpia/0nBGaJKKOmU37Z5MjC/HpD1X+lzNeZ2HvDNQP3x+HPI5f3jarxJ9KhjYVfCut2YOR17oEeON+BzOT6tW7dGamoqlixZYtjWoEEDPPnkk5g1a5bN/eXK8bGVa8M/FRvnL7D2x/pyWBsTs0Gh81p7GldYMSZUAIgKC8LNEhI1hB5NhArT+ja0ECJSo5n2/E6IYljCAQQXyezNa5CzPxc/T/P8mtBo/bHLCqXPj5WIRCD5SWA/OTTbRXii/qNkTThGVNcvV373ssumZcKgn/T/OivfzQvxyxyfyspKHD58GJMmTTLZ3r17d+zd69r+J/Y4JttrNmgM/zT+2pojgnkTHEyXFITmRaLHNq1qR+HgBSfeuFxAWHAASiq1Fp8TnqHta6NrskY0J61HSgK6JWuY89fsdRE3gY9kaCuBtMnAkVXyVolI6bRtC7H8Glf0yCrOI9HjCBmzgCuHgb2LxMckNAG2TnHdnMy5cxVo0FcfdRLNN3K9R4434FPC5/r169BqtYiPNw1Dx8fHIz8/X3CfiooKVFQ8MPkrLi6WZS72iBjWZSpb43qkJGDJS6kWT+OayBBDMinhGKev3nH3FBxCAeCD5/T5OpO+t0xSr6oOQsukaHEBcp8ApcLmGB6Hhb1Q9CQ8AUh7G4ipK1+ViBylt27Pr/GpQL7rUUcB2d9ZH3N6k/TjKpSWeTv2cuMcsKiJddEDuNwjxxvwKeHDozAz6OI4zmIbz6xZszB9+nTZ52CPiLHlnWOcNGoLsafxgzk3mYRPdFgwCksq6c+nCEVl3h0VG9v1EUMSu1CuVlHpXZNkenvtFYxxSNiLRU9u5wO7ZumFipyhfEf7c3lrfg2hJ2e3c35+/VYCYTH6nKzzO4GjX9hxEAUQGgXsmml9mNTop1jbDVe0hHExPiV8YmNjERAQYBHdKSgosIgC8UyePBnjxo0zfF9cXIwaNWo4PBd7RIytZSpAPGlUCKGncdZ5vdMrGSO/sJyHPYQEKVF+V6anHEIWaseqDcn0QtwvhMX0H09CpwNmbLTPXsEYu4W9u0p2HSm9lT2ZVI7fRIKZA5/Kf8y6XYDbl/URn21TGYWV2J3AxmdBHQuMPgoEBrPNTTAXLUp/PuOlWU8yRXQAn6rqCg4ORvPmzZGZmWmyPTMzE+3aCa9xqlQqREREmHzJgb2VL/wylSbS9KlXExlis5Rdznn1bCw8D3uY9XRjfPpSKhJkOBYhD3HhIcw5NyO+YK9MtEaAUoF3eiWLih5ARNhLKdn1FGRruHj/WoREynQ8gonKEvmPeW673pNn7csMokehT4IXaj+SNtl2YnzpdeDSAbZ5ibXdKCu0zEeTqyWMm/GpiA8AjBs3DgMHDkSLFi3Qtm1bLF26FBcvXsTw4cNdPhdruTbWnpalJo06a17G8/g5Ow//25dr1/k0ESFoWzcG3ZI12H/uBkZ+cQS3vHypyFsxjqr89If9oXzjiFC3ZA3TZ3Nzdh5mbBSOMFn9nfDGkl1DY0YHl0tCo/Q3n/JbskyL8BY4/c/9mR/0kUfjpaYT69gOwfL7IDkXzQNNEe3A54TP888/jxs3buDdd99FXl4eUlJSsGnTJtSqVcst87FXxEhJGnXmvIznYY/wqaoOMixdBCgVaF8vFrP7NcJra44AoOC9KzGPqjjaiJapCus+ttqfTHm8PiJDg4VdxJ1pWOgslAFA6mDbeRhCqCKAZi8BD3fXN0h1B8FVgErvTuD3CUqvA42eMd0m5++DXblo3m+K6HPCBwBGjBiBESNGuHsaBpwtYuxFyrzsbVr6crsk0eU884hTFVUg7lTck3B0QgrmUZVWSdHQRIQgv9ixBpK2qrWsGXPyjP76qIm3lEkOkSF6YsNbx9NKdmPqSt9HHQuMOwWc2Qx8PxQok6GXlxQUAUDbkUDXaQ+SWs9sBY5/7dp5EHqExIucvw+OREk9KcIqEZ8UPoT88LlBw+9Halioqg7CqM4Pm2zjq4Mq7ukw/5kmgAK4fqcCsVVUGP/NURI+MhMdFoR3ejeEJsIyopd5Mh/l97SC+0lJpbUVOWLpH2duqGnRokUubx1XIikCdf999P5QL3pc2V8rQAU0exGIeRhoOexBQiz/NB9WjYSPy7EiXuT0mnIkSupJEVaJ+FRyM+FZzH66kcmNdnN2HjrM2YH+y/ZjzFdH8eLyA3jz22NQBSqhVCiQX1xh5WiEFBT3v2Y+1QhPNauOtnVjLH4Wr605Itp2pKo6CJ8MaIaEyBBrjSGQwGCvwOrfY4xFixbeW0co2dOTuk8bY7O1hhH8+6jfy/X+P9oK4Ng3QGQN4SqgskK4ti2Dv8MgXuT6fZDyGTWen70tYTwEivgQTFgrfRbijfs+MTybs/MEo0X8k/3gdrXlmCZxH2vJwixLT6pAJTJSEqBUKhy2V7A3l8gih8hRbx17EfI3YTmn1Sfz+7QZATza88Exc351j//P3Tv6Xl/Pfaa/zvx7Pr2JHKBdTXgC8DhDybgcvw8sn1ETPDjCKgESPgQTLMsVxtSOVRv+X6vjMOn744Lj+OqgtUf+dnCGxDu9GiA2XOVw6wgAyC+uwMGcm3ZXJhpjb34Yj0nEyBFvHXsQ8jeR4mUi5gIdngg0H2yZB+TuvIkfxwDae8DPbwKlLs4vIvRICb7I8fsg9hkNvR/JtfDxcaAljIdAwodgQupyhfFT/kc7/rLayZ0DUFzun7k9ctnSRYcFYXB7y0RyIaS2jnDUXsGaMScLjlaf2Y2YWzTvZcK6pGD+ZH7jnL7HmHHFFy+mQt1cBFF2U+8zQzgPRYA+0qcKF676E/p82Rt1ZEUsegSQczPhv0i9+RSWVALQR3tW/JbjjCn5BHJlcjzVtDqzELGndYSjlYlikSOlwjKxmUdKixbZscct2trNiX8yP7lB32JDUEwN1JeRE75J/d76z0TLYfrPw8IUkYH3Pxv85+vPjY5FHVkRix55acm6NUj4EExIXa6YsfEkMlI0+GjHWea+VlVUStypoNYW9tA1WcM8Vs6ecFIQihwVllRi5BeWnk72tGiRFSlu0bygsXVzsimmQN45voxCCZQWAr/M1Ttx28rlKr4M/DLfilCWEHV0dsTIyyDhQzBhvFzBQl5ROT7acRYfbjvLfA4SPaYMbFMTn+2/aHNcFVWgJJEid084KQhFjpYoHcshcgpS3KJZl8Socal/c8qONg/7P4FVoczioOxonpoPQsKHYIZfrpi09jhTy4mVDF3gCXFSa0Yh82SBbYNBjoNWx0kSKnIkLcuFs1u0WMDy9MvqUaKOBX54DUxLYu5OXCa8D1utSmw5KMuVp+ZjkPAhJNEjJQHhIUF48b+2G+DZ249LASBKHYw7FZWoFPbXczlVVAG4U+HayWgiQ9G/VU18uO2M1XF3KrVoM2sbZj7VSJJgsVdw8CaUcooUl7mbsz79srrjKhTsS2JebPhGeDC3RRoF25On5ieQ8CEk06ZOjM0ckUh1kNVKLmtwAG6WVjoyRUlEhwXhZon1uc7t1xhKpcIiQsJKaKACWihQec/2cp5xjg1rBdbNkrumTseMSBUcm7PzLK5BgruXpViR8vTL6o5bco3t3HeuAg2fkqdxKUEYI/YZlJqn5keQczMhGT5HBLC0nOC/f7ldkkvnZC9V1UF474kUq9YZYcEBUCoV6JGSgD0TO+PLYW3QpX41KCQEOYZ1rMskegD9LdbeRqIGp2MnwLs9mws/3oRyc7bIk6cnwJJYvHmSfhwPizuulIaRygCg+yx7Zk8Q4oRVE94uJU/NzyDhQ9gFnyOiiTS9MWsiQ7DkpVSM6vyw1XYHnsLL7ZLQs3EilryUiqrqIMExpZVaw42dj5AsH9wKi59vxnweezrbAw8qsFiuo7HTsdxYc3u2aC/hibA+/e6apXdP5gVQcl9gbDYw6Ceg33L9v2OPP4gM2bT8v2/vX6O1/rgF2TK+KRsoKaDvF4SLRFrl7OLuY9BvBmE3tnJEHDGtcwXGTVS7JWswbcNJAJZLXvzcJ31/HOGqILS5vzT0/s+nmM8lJd9JAb2I6JaskVxNB9jXG8sWttyeLdpLeBqsT7W/zNN/Gef9WHPHtWn5z+mXuBY1cXyJSx0LNOkPHFoB3C2xPV53T19CzVG1pM9irWeWnF3cfQyK+BAOwUdAnmhq2QhTLCrkKRg3UT2Yc9Nm9dSt0rt4cfkBdJizAx/tOCs516dqaJBdkRv+OkaHCTSQFEAOp2OtjsO+czfww9HL2HfuBvKLypj2c4bokgWpT7V83s9JhhJksSUxnn0fyZPXU3oD2LeYTfTwkOjxbaz1zOJFOQDRpAQv77llLxTxIZxKj5QEdK4fjzaztuNmifMTls2dgKPUQeAAk0RroWRcKTfs/KJySf5EPC+3T8JCGxVaxhjP6cF13CaaiC2X8aBQAjOr6LpwXcJN2ZXYfPo1R2LVS3Jfvcj4dpAMk7U2J4K4T9rbbI1Mhfpw+UjPLXsh4UM4ncO5hS4RPQoAH/VvhqgwlcnSGwCb5ddSoiT23n7qxYVhyUupeHtdNtP1MJ9TcKASM59qZFj2cobxIJ/AbP4eCxl/fl8evIhRneu5x23ZGpK7UAOSql50WmDLZBkm6qfoOOCiFrjNAeEKoGaA/imGEEd3Fzj+nbAXlblX1ehjwKUD5Nx8HxI+hNNxxfKHrZJqW3knjnYQZ2HGxlPYM7GzQ5EbZxkPanUc9p+7gUlrj1tNYLYF39XdI/N8xJ5+bcGSH0SuzPZz6i6wuRwoNvqURSiAHiFAA+GCAwL6XDQe45w0a15VjZ5x/Tw9EBI+hNOREk1JiAzBjZJK5tJvQN+yYfeEdAQH2p+y5kgHcdbxxsm/jkRu5HY6FlracgSPzfMBTLtQn98N/DrP9j4s+UFylQSrY4HS6/Icyxs4dRf4RiB/rJjTb38OJH5Y4HPS2r0O7F0Mcmq2DiU3E06HpSS7qjoInw9tjT0TO2PRC00lHf9OxT1kXXC8hNueZOw3uj4iaTwvCmzZAdiK3FhLKpeCmDePI8iRXO1U+Cqt9MlspegsVS9ylAQ3eh4Yd+r+nPwAHaeP9Fhjc7lp0p4vEhoF1O0CKNny6ITh9F/7PoIkryo/hYQP4XRsGR4qoK+wal8vFgH3jQI/fSkVmggV8zn2nbth9/yMK5giQ4Oxe0I6Pn+lNaqGij9pKqCPTo3q/DD2TOyMd3o1YDqXsSgwNkT89wtN8eWwNtgzsbPLHJCtefPYA39N5O7q7jTkrHqx6efDQFRNIDAYSPGT5YiLWtPlLSGK7+f++CKt/gkkPwmUFQLntgM6GfIgrVbxGeWs+TkkfAiXIBbhiA4LxscDLCMcPVIS8NukLniyKevTr323783ZeegwZwf6L9uPMV8dRf9l+9Fp3k7cLr+L2f0aGYSZ0NleaFkDgF7YDW6fZDWqJSYK5Irc2IMtbx5z9D3Uggz/b/4a4Lyu7k6DxZ2ZBasiipFaHYAT64G9i6TvG6QG2o32rmjRbcbfWdZx3kbeMeDketef1w+dms0h4UO4jB4pCXinVwNEhz2IpNwoqcSMjScF2x0EKBV4tnkNpmO3rRMreT62WjAAsLr09eG2s+gwZ4fB0dlaVIsXSj/9cQX7zt3wCIdjKbk4/Hua9XQjfTTOziU6j8SWO7OU41jz87FGaDRQdhP47mXp+wLA3VLgoZYP3kfHCfYdx5WEMwpE1nGuporGgZ0V+iord+CHTs3mKDiOc/9fYA+iuLgYkZGRKCoqQkREhLun41OIlUrzf9aEbpxaHYfm72VabXhaVR2Ew//qJinSoNVx6DBnh2jEg6+s2jOxMwDgox1/CXZJN5+7UKIw3wrDlpeQszHvqq7TcXhxOdsfX/P5OqNDu89wrxJYUF9vOMhKu9H2RXqMCU8E3sjWR590WmBhigTfIjeg44B/37G+3BWhAMZU8czS9gAVoK1w9ywkcN+peexxny1lZ71/U1UX4RJs9Xoyb9PAE6BUYPbTjTDcSssGYwdmVqS0YGiVFI2vsi6KjjOeu3nF1YXrpVi47YzF++ajSq6KkggJMk1ECKqqg1BUelf01lg1NAgfv5iKNnViLH4uHlmy7glcOsAuesITgYxZwFYZPIBuXwF+mQ+kTTTyLRro+HGdhfJ+ybpQVRdPjxDPFD2Ah4se81pT/3ZqNoeWugiXIEVomCOW7KyJUOFTO4UD6zJPwe1yyXPnRUHvxon4KuuiqNjjALy97jjW/X7ZqctfYkt6V4vLceu+6BFNOu/XCO0fjvWvaI5Oq28oevw704alrLDmUHScoI/QhMXI5wG0ayawaSKw72PvSGJtEAQ8F6qP7BgTodBvp1J2+1CbPZRIzVnzcSjiQ7gEKUJDCLm9a1hLruPCQ+yeO0vy8M2Su3jj66MAnLP8xRJpq6oOgipQifziB0+wjhoiei3Z64FN40wjNsbmcCyw5lAkddI/fcudbHrwU3mP52waBAGPBpJzs5z0mKXv2i7m1Gzu7OxnTs4kfAiXIEVoiCHn8ootp2Zj92ShKJQQ5nOXauTnjOUvlmhVYeldfP5KaygVCv/O2dn6jnCeTfEVaeZvLH3B1LFAjdb6/7/G3r/NZ1EqgNp0O5KN62f0wqfhU5aCxpqzM2/u6eOCiD5phEuQIjRcgTWnZvPSbKlz5xN/z169I2lO1nKd7IVVfF2/U4EnmlZ3+Hxei80yco69YSlLX7DS68CiJkBCU+D0JrunTRCC/DJP/2UerTy54f7nUsjZeeCD6kIeqdFOL4FyfAiXYKvcG3C9Bwyre7KUuRv7An208y/Jc7KW62QPckTafB6dFtg43vY4KeZvLKXtxVdI9BDOhW9VcXKD/nO+eSKsOjuXmf3dMd7fh6CID+EynNVg09E5seQOscxdrFzfHuTqd+XsSJtPlLXn7mXvjyUlHye5L/BID+ml7QQhG/fjyJsnAaoIO5LojfZniXZ6CSR8CJcid5KyHLDmDlmbu9ztH+SKwEhZ0pOKUIm8O/yJHEaKmJFq/ialtJ0gpNL9PaCsyEazXb5VxR47T2LU6iLpMTuP4VnQUhfhctzZpsFRxObO2v5hZFpdRIcFS25t4QiONkQVwpbrtZATt8fCKmbUsWwNS4257UXXgfAi7jfQbTMCiKvPtoujT2U+1OqCIj4EIQOsS1OPaMIx86kUp0RgrCFnpM1eM0qPxVCFZWMZoOcH0kL9JzcAmx00JlQogXrdgbNbbTSgJPwHMzNCZvuEx4Bjn9vv5u1DrS4o4kMQMiAlidgZERgW5Iq0OWJG6ZEYGoxauR7tRgMpT7Ifk6+eYc0dEuPh7sCZzdZFj1p6nzrCTkKi9GXixoQ6sRK19XDLxrPmZoQsS6kR1YHaHYDus2CX6AmNlh7t9GAo4kMQMiA1idgTc51YcdSM0iPhq7DM/U3UsUCvD4CGT7Ify2r1jAQeeRw487P1MepYYPQxYE4Nigi5gjavAR3fBC7s0bt6KwCERAJb/+Wc89XvDWTMFPfW0WmBLQxRxe4zgT83WmmNImK7wFN2U7+/j5S1k/AhCBmwJ4nYW/td+WyJfHJfeQzccveyVc8EhwP1ugGX9puOV0UAvT8Efp5o+xil14HtU0n0uIroJH0/tAOfAGW3HmxXKOX/GURUf/D5E0sqZv2sXT8N7LIW7bEl0n2rsouED0HIhCeW6zsDTzOjlBVrNxlWWJNAK28DJ74HQqoCQWrgbql+e0WxXvSwLpNl/deuaRJ2sOF14J5AJFN24akAUgcBJ9bpo3oKBVByzVKMs37WDnwCxyKQvlXZRcKHIGTEm5ewWHFmibxPIDUJtPyW5TZHc4MI5yAkekywsWQkREgUUF744PvQaP0xds0UHh+RCGTM0jcivfYn2zmMo1OO4COVXSR8CEJmvHUJSwr+Et2yC5ZeXYSPwv+8JQigoFDgudX6iM6Nc+KCh6f4CvDtIMb5KIDQKEtHZnvxkcouEj4EQdiFP0S37IKlVxfh40j4md++os8RavgUsDBFxjnc/z1sPdy2mGI5VkSiz1R2kfAhCMJu/CG6ZRdiVWIEIQTfs03Oz0pEot7rp34v4MgqCRFIkQVs3jfIByDh4+f4RK8lgvBEjKvEzu+20VaA8Gv++AbQyZQg/dgEoE4n0yRoQwTSGgqg3etA9nemAowXUD5Syg54ifC5cOECZsyYgR07diA/Px+JiYl46aWXMGXKFAQHBxvGXbx4ESNHjsSOHTsQGhqKAQMGYP78+SZjiAf4TK8lgvBU+CqxkmugZS9ClNLrwLEv5DlWXH3LyitbEciI6g/ETddpjls6eDheIXz+/PNP6HQ6/Oc//8HDDz+M7OxsDBs2DCUlJZg/fz4AQKvVolevXqhWrRr27NmDGzduYNCgQeA4DosXL3bzO/A8xDqJ872WnOkgTBBuQad1zx/0re8Aexc5/zyEd1NRLM9xxBKQjSOQt/P0Yjysmt6J2vh3QQ5LBw9HwXGcVz6CzJs3D0uWLMH58+cBAD///DN69+6NS5cuITFRb/H91VdfYfDgwSgoKEBERATTcYuLixEZGYmioiLmfbwNrY5Dhzk7RNsO8D4seyZ2duqyFy2zES7j5AbLp92IRP0SgDND+CfWS6jAIQhHuJ+APPa4z0VoWGG9f3tFxEeIoqIiREc/MEjbt28fUlJSDKIHADIyMlBRUYHDhw8jPT1d8DgVFRWoqKgwfF9cLJPq9mCk9FpyVuIqLbMRkrE3YsP3zTKPbxbn6bcb9z2Se74bx8t/XAMKvedLGUOvJsLH8b0EZGfilU1Kz507h8WLF2P48OGGbfn5+YiPNw3xRUVFITg4GPn5+aLHmjVrFiIjIw1fNWrUcNq8PQV391ril9nMxRe/zLY5O88p5yW8mJMb9KW+q3sDa4fq/12Yot9uDat9s+5v2zxJP05uLuxxshEhBzR5nm2oprET50G4HfPGpYRV3Cp8pk2bBoVCYfXr0KFDJvtcuXIFPXr0wLPPPotXXnnF5DWFwnKZhOM4we08kydPRlFRkeHr0qVL8rw5D8advZa0Og7Tfzxp7TaE6T+ehFbnlSuwhDPgIzbmSZl8xMaa+LHZy8jIil9OTm4AvrVVReMgTV4EHu3JNjZjJvDsan1/MMI3yJgJ9FsODPpJv7xFoocZty51jRo1Ci+88ILVMbVr1zb8/5UrV5Ceno62bdti6dKlJuM0Gg0OHDhgsq2wsBB37961iAQZo1KpoFKppE/ei3FnryVPWGYjvAidFvhxDMQjNjaaJ7Ja7MtpxX9yA/DNQAk78A9mEsW+KozBJfp+3keN1sCeD/X9wQjPJe1t4PBKffKxKPd/pq2H07KWnbg14hMbG4v69etb/QoJ0UcdLl++jLS0NKSmpmLlypVQKk2n3rZtW2RnZyMv78EHZuvWrVCpVGjevLlL35enw/daAh78yeVxdq8ldy+zEV7GL/Nt2O3biNiwWuzLZcVvEGoSaDvy/v9I/H2rWuuBS7Tg/ve/T+kHLGoig3sv4TwU+pLyjm8Cb5zQCyCxcQDl8jiIV+T4XLlyBWlpaahRowbmz5+Pa9euIT8/3yR3p3v37khOTsbAgQPx+++/Y/v27XjzzTcxbNgwn63OcgS+15Im0nQ5SxMZ4tRSdncusxFehk57v6s0A2IRGz4iIioq7t9w5LLiv7BHWl+kdqOBjPf1+RkREn/nYh4Bcn4FtJVA2mTL/SMS9YZ0exeTe7RHYyZmlAFA2kTguc/uf3aNoFweWfCKqq6tW7fir7/+wl9//YWHHnrI5DW+Gj8gIAAbN27EiBEj0L59exMDQ0IYd/RacucyG+Fl5O5l7yotFrGx2jfLCU/POb+yjQsKBZ78FGj4pP57Y4+VUxuAg0ut7g4AWP9PoMyoq3d4gj5SEFNX78+ivQd8OxDOMU20YsYYGgXcuwvcveOE8/ogYs7Ixp8JHzYTdAde6+PjLPzBx8fd8FVdgOBtiMwTCT3Hv9NXcNkiNBqY8Jf1G4Kgj091+a34d7wH/MLQmuKx8UCX/xN+LedXfdWaZO7/Bgm1HXAVaW8DnA7YPdv15/ZGMmZSro6M+LyPD+G98Mts5j4+GvLxIYy5cY5tHMuNw1VPz7U6AGAQPrU7WjmGrYRlMe6PdadL9N7FlEDNhIQEZXc5jvswJHwIt+COZTbCizi5Adg1y/a40Gh9QigLrrDiT3pMv9RjvARlTmi09XlYXZ7zcEj0sMOyxOoux3EfxyuSmwnfJECpQNu6MXiiaXW0rRtDoofQY9V00Iw+//asp19lANDHRsSFZc58U0nzhOXQKMfmJ0RIlHOOS4jT7nXbwsUR/yrCKiR8CILwLGyaDt4n7W3PfOpN7quvyAk3Ey3hifrtrHNO7guMzdYb1PFGdc+ulneuTQcAb50DJpzT55sQriF7rXW3cHc6jvsBtNRFEIRnwWomGFPXufNwBEdyiqzldOi0gDpWnlYYweFA348eHFsuLyPCNrz3lNiSpxTHcW/opO5heUokfAiC8CxcbTroLOzJKbKV06EMABo/B+xn9DeyRupA05uPJ1zP0BgXNF31kLwpawLfHY7jzsID85RoqYsgCM/C1aaDngJrTgdrfy5bmB/H5nV3Ab0WuGAOHKAMYhin0Oc/KZx0m7QmNFlF6J2rnr3c5aF5SiR8CILwLFjaMPiaZb+UnA6DQLEXEeFo9bq7gEA1kNzHaA5ORHeXbVzfRcAzK0VeVOi/7EkMV8daF+6sInTL28DCFM9MdPbgPCUSPgRBeB5iVU2+atkvJafDIFAcECdiwlHsuruCe6XAd0P1c2j3umvOKSZaQqP1bUDq99K7a1trH2Grik+Ixs9ZF+5SRKinVnlJ+Uy7GMrxIQjCM/Eny357cjpCqwr4BdnIX1EEAM+ssC4c+eu+e67rHZhPrgO2PAScWOua8wWGAv9Yrb+u53YBpzcC5bf0/dZ2zQSOrHqQi2Lts/jcZ/rmtKx92liWK3kRap4fYwEHQKGPntTv5Tm/Hx6cp0TChyAIz8UVpoOegJSEbj5vwtoSghicFlDHsJ3r9/+xjZOb/R/r2164gttX9Dk8gSHAsS9gcf34aAofZRT7LPLC6Pxu4JuBQKVYn7L7js2s+Wn8cQ98ql/WEsUDq7w8uEiBlroIgiDcDWtCd43W7OaOYrA8YbN6KTkDV4kentt5NnJROLZcFGUA8HBnoMUQ6+Ok5qcpA6QlO3sKHlykQMKHIAhCCJ1W3zD0+Hf6f52ZhMma0H3pgOOChOUm6kk3UGdTcs32NWXNRTm5Qd+vTAwWx2YhPDh6IooHFymQ8CEIgjDn5AZ9tczq3voO8at7O796hiWh2yFBIuEJ25NuoCwEhQHB4t24RQmNAkoZ83JOb9L/KyaIbbZaUdh2bBbDg6MnVvHQIgXK8SEIgjBGLIfGPN9DLsxdbUcf00d2hJJoHRIkHPsTtt0d4t3E3RLgpfXA90OBUgkGiGWFwK/z2cb+8Y1+qXHLZNMIUWhVoPUI/WvOclu22rjWwy0ePLBIQcFxnBd8ql1HcXExIiMjUVRUhIgIO54gCILwXnRafWRH9AZ2Pzl17HF5/nCzuNoaC6OwasC6V4Hb+ZAsSNqMAHowdLznObEe+HaQtHO4k37LgYBgfXKxOwgOAypLbI/rtxxo9Ix95xD8vFTXix5fs3iwA9b7N0V8CIIgeFzZI4klsgRY3uhCo2EoYZYifqQ4Pp/coI9seBNV4vU/k+c+A34cLVDq72RYRA/gWNTOA6Mn3ggJH4IgCB5XeY/YdLVV3PeFKbQcw9/QBX18hLBRQm2+1FZ6A/h2sMjcnEBkDaDokmPHMM5v4cXBL/OBA0tcL4BEsfJzkNLE018sHpwICR+CIAgeV1XPsESWRM3w7gujIDXwzCp9p/Yb5/SGe1LzP4SWThRKuDSvx1HRA4Xl+1MGAGkTgY5vMnjguAIrPwcPbOLp61BVF0EQBI+rqmccLhe/v+SmDNDni6RNvN9WQUL1jFgDSVf76AD3xZaV1gxijUIjqj94f0LVVlI8cOQktKrZPEV+Dh7axNPXoYgPQRAEj6uqZ26cc2x/HmMBJSX/w2bptYsxiC2Ra95vJRAWozcbLLmmT/IOT3jw/qxFTdwhfJ79n16sWfs5sCx3elobCh+BhA9BEIQxYj2SIhLlqZ7RafU9oOTA/KbOmv8htzNzaFWg7JZjx2gzAji5Xvo1t5Uk/uwqF5bm38/jqd3BtlhxZSI9YQIJH4IgCHOcWT3DKjpU4UDFHQjfrCX2fDJHbmdmPsJxehOw/xP7jvFoT6D7ew+uuToWUCj0EZ6cX+2Pmmx5G+g+C/huMCRXwklCYkTQg5t4+jokfAiCIIRwVvUM642s2UBg/xI4ZclNzuWfiOoPIhxlhXYIHyMRx1/zkxuAH16znfDLGjUJi2HsdM4wVwBo+BRwfqdpxZjUiKA3tqHwEUj4EARBuBLWG9mjPYGabZ2z5CanM3PGTL1gMURfpCAg4qQ4Z0uJmjR6Rp9L5Igpo/G1l1KCLoTNn4GDUT1CFBI+BEEQrkTKDU8Z4JwlN0MStwwux+oY/b/25A2FJwCPm7lUS0n4lRI10WntN2VsM0IvRI2vvaMRQW9uQ+HlUDk7QRCEK5HatZq/wTZ6Rv+vXDfC5L5Amgz+NnzzTntyUZ761LQUfecs9oRfQJr9gD3CLKK63iagxyx5rz2Phzbx9HUo4kMQBOFqnF05xkpMXceP8cc3+qRke3JRSq4Jl6LbghdZUqImrMLssQlAXH3XtYOgNhQuh4QPQRCEO/CEG54cibOl1/XvwZ68oRvngF2z2MfzGM9bTESqY4DGzwGhUfqIEut7rdPJ9eXj1IbCpZDwIQiCcBfuvuHJleR856pZ9MUWCn1+z5FVEs8rkvBrLCJPbwL++FovyPZ/ov+KSNSXtDsjmdjRJGfC5VCOD0EQhL/Ckm/EAh9NSe4LtHudbZ/mg+0oLefEE34N5fRL9I1WjSnO0/v4pDxzfwNDbhULJzcAC1OA1b2BtUP1/y5MoVYTHg4JH4IgCH/GWoLts6vvJw+LYda7TKcFsr+zfj5FgL65qhz5RcbYrAgDkL32vpOzDMnE1GfLa6GlLoIgCH/A2pKMtXwjhdJo+cpGyTVL5RSn1RsK2oWV/lWsZobqGGBstmPLU9Rny6sh4UMQBOHrWGviyUc5xPKNpFSgSTEUbPiUHflFVvpXSTm3o7lV1GfLqyHhQxAE4ctIcUIWg7UCTYqhoNVSdBsIiRxXtoCgPlteDeX4EARB+Co2l2Q44OeJ+nG2YDFSlGIoCIjnF9lCSLxIPbcjUJ8tr4aED0EQhK/CknNz+wrwy3x5zifVlRrQi5+x2cCgn4Cnlz1ogSGIFfFiz7ntxZUii5AdEj4EQRC+CutSy66Z8lUh2dOGgY8mNX4O6L0QekFhh3hxVQsIV4osQnYUHMc52JrXtyguLkZkZCSKiooQERHh7ukQBEHYT86vem8ZFiKqA2OPs92sWUz7HDH2E0zGrs7ezsNVpoJC8wyNAlq/BnR8k4SPi2G9f5PwMYOED0EQPoNOqzfUYzUKHPST7SoklgoxOfAWR2SdVr9UeGCJ3kCRxxnXhLAK6/2blroIgiB8FZMlGQZsLY250rTPWV3p5ebPjfp+Y8aiByAjQw+GhA9BEIQvk9wXSHubbay1KiQWZ+TNk9gqxHwFuiZeidcJn4qKCjRt2hQKhQJHjx41ee3ixYvo06cPwsLCEBsbi9GjR6OystI9EyUIgvAUOr4prfWEEFJM+/wFuiZeidcJn7feeguJiZa/wFqtFr169UJJSQn27NmDr776CmvXrsX48ePdMEuCIAgPwrDkZWe1FECmfULQNfFKvEr4/Pzzz9i6dSvmz7f0nNi6dStOnjyJNWvWoFmzZujatSs++OADLFu2DMXFxW6YLUEQhAfhaKk3mfZZQtfEK/GalhVXr17FsGHDsH79eqjVaovX9+3bh5SUFJNoUEZGBioqKnD48GGkp6cLHreiogIVFRWG70kkEQThs7C2nhCCN+0T7a2l0L/uT6Z9dE28Eq+I+HAch8GDB2P48OFo0aKF4Jj8/HzEx5uq6qioKAQHByM/P1/02LNmzUJkZKThq0aNGrLOnSAIwqOwt1qKTPssoWvilbhV+EybNg0KhcLq16FDh7B48WIUFxdj8uTJVo+nUFjah3McJ7idZ/LkySgqKjJ8Xbp0yeH3RRAE4ZO4yhnZm6Br4nW4dalr1KhReOGFF6yOqV27Nt577z3s378fKpXK5LUWLVrgxRdfxOrVq6HRaHDgwAGT1wsLC3H37l2LSJAxKpXK4rgEQRCECI4sl/kqdE28Cq9wbr548aJJ7s2VK1eQkZGB7777Dq1bt8ZDDz2En3/+Gb1798bff/+NhAS98v76668xaNAgFBQUMLswk3MzQRAEQXgfrPdvr0hurlmzpsn3VapUAQDUrVsXDz30EACge/fuSE5OxsCBAzFv3jzcvHkTb775JoYNG0YChiAIgiAIAF6S3MxCQEAANm7ciJCQELRv3x7PPfccnnzyScHSd4IgCIIg/BOvWOpyJbTURRAEQRDeBzUpJQiCIAiCMIOED0EQBEEQfgMJH4IgCIIg/AYSPgRBEARB+A0kfAiCIAiC8BtI+BAEQRAE4Td4hYGhK+Gr+6lLO0EQBEF4D/x925ZLDwkfM27fvg0A1KWdIAiCILyQ27dvIzIyUvR1MjA0Q6fT4cqVKwgPD7fa1d0ZFBcXo0aNGrh06RKZJ5pB10Ycujbi0LURh66NOHRtrOOp14fjONy+fRuJiYlQKsUzeSjiY4ZSqTT0/3IXERERHvVh8iTo2ohD10Ycujbi0LURh66NdTzx+liL9PBQcjNBEARBEH4DCR+CIAiCIPwGEj4ehEqlwtSpU6FSqdw9FY+Dro04dG3EoWsjDl0bcejaWMfbrw8lNxMEQRAE4TdQxIcgCIIgCL+BhA9BEARBEH4DCR+CIAiCIPwGEj4EQRAEQfgNJHw8gAsXLmDo0KFISkpCaGgo6tati6lTp6KystJk3MWLF9GnTx+EhYUhNjYWo0ePthjjq3zyySdISkpCSEgImjdvjl9//dXdU3Ips2bNQsuWLREeHo64uDg8+eSTOH36tMkYjuMwbdo0JCYmIjQ0FGlpaThx4oSbZuw+Zs2aBYVCgbFjxxq2+fu1uXz5Ml566SXExMRArVajadOmOHz4sOF1f70+9+7dw7/+9S/D3946derg3XffhU6nM4zxl2vzyy+/oE+fPkhMTIRCocD69etNXme5DhUVFXj99dcRGxuLsLAw9O3bF3///bcL3wUjHOF2fv75Z27w4MHcli1buHPnznE//PADFxcXx40fP94w5t69e1xKSgqXnp7OHTlyhMvMzOQSExO5UaNGuXHmruGrr77igoKCuGXLlnEnT57kxowZw4WFhXG5ubnunprLyMjI4FauXMllZ2dzR48e5Xr16sXVrFmTu3PnjmHM7NmzufDwcG7t2rXc8ePHueeff55LSEjgiouL3Thz13Lw4EGudu3aXOPGjbkxY8YYtvvztbl58yZXq1YtbvDgwdyBAwe4nJwcbtu2bdxff/1lGOOv1+e9997jYmJiuJ9++onLycnhvv32W65KlSrcwoULDWP85dps2rSJmzJlCrd27VoOALdu3TqT11muw/Dhw7nq1atzmZmZ3JEjR7j09HSuSZMm3L1791z8bqxDwsdDmTt3LpeUlGT4ftOmTZxSqeQuX75s2Pbll19yKpWKKyoqcscUXUarVq244cOHm2yrX78+N2nSJDfNyP0UFBRwALjdu3dzHMdxOp2O02g03OzZsw1jysvLucjISO7TTz911zRdyu3bt7l69epxmZmZXKdOnQzCx9+vzcSJE7kOHTqIvu7P16dXr17ckCFDTLY9/fTT3EsvvcRxnP9eG3Phw3Idbt26xQUFBXFfffWVYczly5c5pVLJbd682WVzZ4GWujyUoqIiREdHG77ft28fUlJSkJiYaNiWkZGBiooKk5C1r1FZWYnDhw+je/fuJtu7d++OvXv3umlW7qeoqAgADJ+RnJwc5Ofnm1wnlUqFTp06+c11GjlyJHr16oWuXbuabPf3a7Nhwwa0aNECzz77LOLi4tCsWTMsW7bM8Lo/X58OHTpg+/btOHPmDADg2LFj2LNnD3r27AnAv6+NMSzX4fDhw7h7967JmMTERKSkpHjctaImpR7IuXPnsHjxYnzwwQeGbfn5+YiPjzcZFxUVheDgYOTn57t6ii7j+vXr0Gq1Fu89Pj7ep9+3NTiOw7hx49ChQwekpKQAgOFaCF2n3Nxcl8/R1Xz11Vc4cuQIsrKyLF7z92tz/vx5LFmyBOPGjcPbb7+NgwcPYvTo0VCpVPjHP/7h19dn4sSJKCoqQv369REQEACtVov3338f/fv3B0CfHR6W65Cfn4/g4GBERUVZjPG0v9UU8XEi06ZNg0KhsPp16NAhk32uXLmCHj164Nlnn8Urr7xi8ppCobA4B8dxgtt9DfP36C/vW4hRo0bhjz/+wJdffmnxmj9ep0uXLmHMmDFYs2YNQkJCRMf547UBAJ1Oh9TUVMycORPNmjXDq6++imHDhmHJkiUm4/zx+nz99ddYs2YNvvjiCxw5cgSrV6/G/PnzsXr1apNx/nhthLDnOnjitaKIjxMZNWoUXnjhBatjateubfj/K1euID09HW3btsXSpUtNxmk0Ghw4cMBkW2FhIe7evWuhwn2J2NhYBAQEWDwxFBQU+PT7FuP111/Hhg0b8Msvv+Chhx4ybNdoNAD0T10JCQmG7f5wnQ4fPoyCggI0b97csE2r1eKXX37BRx99ZKh+88drAwAJCQlITk422dagQQOsXbsWgH9/diZMmIBJkyYZ/k43atQIubm5mDVrFgYNGuTX18YYluug0WhQWVmJwsJCk6hPQUEB2rVr59oJ24AiPk4kNjYW9evXt/rFP6FevnwZaWlpSE1NxcqVK6FUmv5o2rZti+zsbOTl5Rm2bd26FSqVyuQPvq8RHByM5s2bIzMz02R7Zmamx/0yOROO4zBq1Ch8//332LFjB5KSkkxeT0pKgkajMblOlZWV2L17t89fpy5duuD48eM4evSo4atFixZ48cUXcfToUdSpU8dvrw0AtG/f3sL64MyZM6hVqxYA//7slJaWWvytDQgIMJSz+/O1MYblOjRv3hxBQUEmY/Ly8pCdne1518pdWdXEAy5fvsw9/PDDXOfOnbm///6by8vLM3zx8OXsXbp04Y4cOcJt27aNe+ihh/yqnH358uXcyZMnubFjx3JhYWHchQsX3D01l/Haa69xkZGR3K5du0w+H6WlpYYxs2fP5iIjI7nvv/+eO378ONe/f3+fLLtlwbiqi+P8+9ocPHiQCwwM5N5//33u7Nmz3Oeff86p1WpuzZo1hjH+en0GDRrEVa9e3VDO/v3333OxsbHcW2+9ZRjjL9fm9u3b3O+//879/vvvHABuwYIF3O+//26wDWG5DsOHD+ceeughbtu2bdyRI0e4zp07Uzk7IczKlSs5AIJfxuTm5nK9evXiQkNDuejoaG7UqFFceXm5m2btWj7++GOuVq1aXHBwMJeammoo4/YXxD4fK1euNIzR6XTc1KlTOY1Gw6lUKq5jx47c8ePH3TdpN2IufPz92vz4449cSkoKp1KpuPr163NLly41ed1fr09xcTE3ZswYrmbNmlxISAhXp04dbsqUKVxFRYVhjL9cm507dwr+jRk0aBDHcWzXoaysjBs1ahQXHR3NhYaGcr179+YuXrzohndjHQXHcZyro0wEQRAEQRDugHJ8CIIgCILwG0j4EARBEAThN5DwIQiCIAjCbyDhQxAEQRCE30DChyAIgiAIv4GED0EQBEEQfgMJH4IgCIIg/AYSPgRBEARB+A0kfAiC8Bn69OmDrl27Cr62b98+KBQKHDlyBGPGjEHz5s2hUqnQtGlT106SIAi3QsKHIAifYejQodixYwdyc3MtXluxYgWaNm2K1NRUcByHIUOG4Pnnn3fDLAmCcCckfAiC8Bl69+6NuLg4rFq1ymR7aWkpvv76awwdOhQAsGjRIowcORJ16tRxwywJgnAnJHwIgvAZAgMD8Y9//AOrVq2CcRvCb7/9FpWVlXjxxRfdODuCIDwBEj4EQfgUQ4YMwYULF7Br1y7DthUrVuDpp59GVFSU+yZGEIRHQMKHIAifon79+mjXrh1WrFgBADh37hx+/fVXDBkyxM0zIwjCEyDhQxCEzzF06FCsXbsWxcXFWLlyJWrVqoUuXbq4e1oEQXgAJHwIgvA5nnvuOQQEBOCLL77A6tWr8fLLL0OhULh7WgRBeACB7p4AQRCE3FSpUgXPP/883n77bRQVFWHw4MEmr//111+4c+cO8vPzUVZWhqNHjwIAkpOTERwc7PoJEwThMhSccekDQRCEj7Bv3z60a9cO3bt3x5YtW0xeS0tLw+7duy32ycnJQe3atV00Q4Ig3AEJH4IgCIIg/AbK8SEIgiAIwm8g4UMQBEEQhN9AwocgCIIgCL+BhA9BEARBEH4DCR+CIAiCIPwGEj4EQRAEQfgNJHwIgiAIgvAbSPgQBEEQBOE3kPAhCIIgCMJvIOFDEARBEITfQMKHIAiCIAi/gYQPQRAEQRB+w/8DXOHfTVTV3UwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure()\n",
    "\n",
    "\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0], X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1], X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2], X.loc[:,'V2'][y==2])\n",
    "plt.title('un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1],c = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07f7734e-eeca-4101-888e-3214f58eeb9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cc/miniconda3/envs/TF_GPU/lib/python3.10/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 预测 V1 80, V2 60\n",
    "y_predict_test = KM.predict([[80,60]])\n",
    "print(y_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b30ffde-8cf0-47f8-8691-8ff2d2c7ae15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels\n",
      "2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: count, dtype: int64 0    1149\n",
      "1     952\n",
      "2     899\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 准确率计算\n",
    "y_predict = KM.predict(X)\n",
    "print(pd.Series(y).value_counts(),pd.Series(y_predict).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a68fb8ff-e6b0-4d9e-a35f-771ef1d82de5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.31966666666666665\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y,y_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b167d62-87c2-4e4d-84c6-8497085d4843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "fig4 = plt.subplot(211)\n",
    "\n",
    "\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0], X.loc[:,'V2'][y==0],s=1)\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1], X.loc[:,'V2'][y==1],s=1)\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2], X.loc[:,'V2'][y==2],s=1)\n",
    "plt.title('un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1],c = 'r')\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig5 = plt.subplot(212)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_predict==0], X.loc[:,'V2'][y_predict==0],s=1)\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_predict==1], X.loc[:,'V2'][y_predict==1],s=1)\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_predict==2], X.loc[:,'V2'][y_predict==2],s=1)\n",
    "plt.title('predicted un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1],c = 'r')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c1d06bf6-dd44-4734-ab30-63e5b5fb2df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.997\n"
     ]
    }
   ],
   "source": [
    "# 数据矫正\n",
    "y_corrected = []\n",
    "for i in y_predict:\n",
    "    if i == 0:\n",
    "        y_corrected.append(2)\n",
    "    elif i == 1:\n",
    "        y_corrected.append(1)\n",
    "    elif i == 2:\n",
    "        y_corrected.append(0)\n",
    "accuracy = accuracy_score(y,y_corrected)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c425bab0-2908-4415-95ee-9ffa4bdeeba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "y_corrected = np.array(y_corrected)\n",
    "print(type(y_corrected))\n",
    "fig6 = plt.subplot(211)\n",
    "\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0], X.loc[:,'V2'][y==0],s=1)\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1], X.loc[:,'V2'][y==1],s=1)\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2], X.loc[:,'V2'][y==2],s=1)\n",
    "plt.title('un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1],c = 'r')\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig7 = plt.subplot(212)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_corrected==0], X.loc[:,'V2'][y_corrected==0],s=1)\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_corrected==1], X.loc[:,'V2'][y_corrected==1],s=1)\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_corrected==2], X.loc[:,'V2'][y_corrected==2],s=1)\n",
    "plt.title('predicted un-labeled data')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0, label1, label2),('label0', 'label1', 'label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1],c = 'r')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf6e2bd-07b6-45ee-8ba0-ea2913c7b41f",
   "metadata": {},
   "source": [
    "## KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9fa15c0-58df-4ba8-ab6b-62e67626f48e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          V1         V2\n",
      "0   2.072345  -3.241693\n",
      "1  17.936710  15.784810\n",
      "2   1.083576   7.319176\n",
      "3  11.120670  14.406780\n",
      "4  23.711550   2.557729\n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# data.head()\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "932c69b0-94f7-4eb3-aea5-47d47fdd2fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立模型\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors=3)\n",
    "KNN.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "56789518-e376-48c5-8aa7-7b80953a2311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cc/miniconda3/envs/TF_GPU/lib/python3.10/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_predict_test_KNN = KNN.predict([[80,60]])\n",
    "print(y_predict_test_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f593a4f7-78d6-4b6a-b44f-7a4dd2dd4a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.997\n"
     ]
    }
   ],
   "source": [
    "y_predict_KNN = KNN.predict(X)\n",
    "accuracy = accuracy_score(y,y_corrected)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9edd1dcd-62ab-416b-a527-993b31be890b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels\n",
      "2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_count = pd.Series(y).value_counts()\n",
    "y_predict_count = pd.Series(y_predict_KNN).value_counts()\n",
    "print(y_count)\n",
    "print('\\n')\n",
    "print(y_predict_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70ef5044-27c5-4c1f-9fe1-213df840a6c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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